This directory is created automatically and some papers may be mislabeled. Only document within the CiteSeer database are listed. The directory is intended to provide entry points for browsing the database and is not intended to be authoritative. Papers may not appear in all relevant categories. For example, papers in a sub-category may not appear in higher level categories.
2154.5 Authoritative Sources in a Hyperlinked Environment - Kleinberg (1999)(Correct)
The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set o... / Search and Retrieval-information filtering H. . Information
484.0 Letizia: An Agent That Assists Web Browsing - Lieberman (1995)(Correct)
Letizia is a user interface agent that assists a
user browsing the World Wide Web. As the
user operates a conventional Web browser such
as Netscape, the agent tracks user behavior and
attempts to anti... / retrieval and information filtering Sheth and Maes br of relevant material. Information filtering paints the user as the
400.0 The Ponder Policy Specification Language - Damianou, Dulay, Lupu, Sloman (2001)(Correct)
The Ponder language provides a common means of
speciing security policies that map onto various access
control implementation mechanisms for firewalls,
operating systems, databases and Java. It suppor... / Windows NT . . Information Filtering Policies FiReting br of the action. Example Information filter policy inst auth filter
352.9 Object Management Group - Object Management (1991)(Correct)
this document.
Clarification of Responses unknown Object Management Group
Object Management Group
Framingham Corporate Center
492 Old Connecticut Path
Framingham, MA 01701-4568
USA
email:info@omg.org... / and service brokerage information filtering service negotiation
263.7 NewsWeeder: Learning to Filter Netnews - Lang (1995)(Correct)
A significant problem in many information
filtering systems is the dependence on the user
for the creation and maintenance of a user
profile, which describes the user's interests.
NewsWeeder is a ... /
227.1 Experience With a Learning Personal Assistant - Mitchell, Caruana, Freitag.. (1994)(Correct)
Personal software assistants that help users with tasks like finding information, scheduling calendars, or managing work-flow will require significant customization to each individual user. For exampl... / evolve agents for personal information filtering Exemplar-based br Delivery An Analysis of Information Filtering Methods Communications
217.3 SIFT - A Tool for Wide-Area Information Dissemination - Yan (1995)(Correct)
The dissemination model is becoming increasingly
important in wide-area information system. In this
model, the user subscribes to an information dissemination
service by submitting profiles that descr... / information. The Stanford Information Filtering Tool SIFT is a tool to br aka. alert information filtering selective dissemination
177.1 Boosting and Rocchio Applied to Text Filtering - Schapire, Singer, Singhal (1998)(Correct)
We discuss two learning algorithms for text filtering: modified
Rocchio and a boosting algorithm called AdaBoost. We show
how both algorithms can be adapted to maximize any general
utility matrix that... / available electronically information filtering systems that automatically br for text filtering. In an information filtering scenario once several
172.7 Dynamic Distance Maps of the Internet - Theilmann, Rothermel (1999)(Correct)
There is an increasing number of Internet applications that attempt to optimize their network communication by considering the network distance across which data is transferred. Such applications rang... / Agents for Distributed Information Filtering. Proc. Joint Symposium
171.4 Item-based Collaborative Filtering Recommendation Algorithms - Sarwar, Karypis, Konstan, Riedl (2001)(Correct)
Recommender systems apply knowledge discovery techniques to the problem of making personalized
recommendations for information, products or services during a live interaction. These systems, especial... / and practice and in both information filtering applications and br Learning Collaborative Information Filters. In Proceedings of ICML
161.7 PHOAKS: A System for Sharing Recommendations - al. (1997)(Correct)
blem through a collaborative filtering approach. PHOAKS
works by automatically recognizing, tallying, and redistributing recommendations
of Web resources mined from Usenet news messages.
A collabor... / U.and Maes P. Social information filtering Algorithms for br a general architecture for filtering information from electronic messages
150.7 Intelligent Agents: An Emerging Technology for Next Generation.. - Magedanz, Rothermel, Krause (1996)(Correct)
The telecommunications environment is changing its face towards an open market of information services where the vision is "information any time, at any place, in any form". Within this electronic mar... / information retrieval information filtering smart messaging br to intelligent routing information filtering and service interworking
142.0 Distributed Intelligent Agents - Sycara, Decker, Pannu, Williamson.. (1996)(Correct)
We are investigating techniques for developing distributed and
adaptive collections of agents that coordinate to retrieve, filter and
fuse information relevant to the user, task and situation, as well... / in the retrieved information filtering away irrelevant or br whose main task is information filtering to alleviate the user's
142.0 Social Information Filtering: Algorithms for Automating "Word of.. - Shardanand, Maes (1995)(Correct)
This paper describes a technique for making personalized recommendations from any type of database to a user based on similarities between the interest profile of that user and those of other users. I... / Social Information Filtering Algorithms for br by using social information filtering were tested and compared.
128.5 Content-Based Book Recommending Using Learning for Text Categorization - Mooney, Roy (2000)(Correct)
Recommender systems improve access to relevant products
and information by making personalized suggestions based
on previous examples of a user's likes and dislikes. Most existing
recommender systems ... / Recommender systems information filtering machine learning text br Learning collaborative information filters. In Proceedings of the
127.2 Efficient Concurrency Control for Broadcast Environments - Shanmugasundaram (1999)(Correct)
Maha 98] S. Mahajan et al., "Grouping Techniques for Update Propagation in Intermittently Connected Databases," Proc. Int'l Conf. on Data Eng, 1998, pp. 46-53. [Mass 96] A. Massari et al., "Support... / Signature Techniques for Information Filtering in Wireless and Mobile
119.1 Experiences with Selecting Search Engines using Meta-Search - Dreilinger (1997)(Correct)
Search engines are among the most useful and high profile resources on the Internet. The
problem of finding information on the Internet has been replaced with the problem of knowing
where search engin... / and the Stanford Information Filtering Tool specialize br Tak Woon Yan. Stanford Information Filtering Tool SIFT
119.1 Experiences with Selecting Search Engines Using Metasearch - Dreilinger (1997)(Correct)
This article describes and evaluates SavvySearch, a metasearch engine designed to intelligently select and interface with multiple remote search engines. The primary metasearch issue examined is the i... / and the Stanford Information Filtering Tool Yan and
116.0 A Learning Approach to Personalized Information Filtering - Sheth (1994)(Correct)
A personalized information filtering system must specialize to current interests of the user
and adapt as they change over time. It must also explore newer domains for potentially
interesting informat... / Approach to Personalized Information Filtering by Beerud Dilip br Approach to Personalized Information Filtering by Beerud Dilip Sheth
114.2 Tailoring the Interaction with Users in Web Stores - Ardissono, Goy (2001)(Correct)
We describe the user modeling and personalization techniques adopted
in SETA, a prototype toolkit for the construction of adaptive Web stores which
customize the interaction with users. The Web stor... / like those exploited in the information filtering research e.g.
99.9 Probabilistic Latent Semantic Analysis - Hofmann (1999)(Correct)
Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two--mode and co-occurrence data, which has applications in information retrieval and filtering, natural lan... / from information retrieval information filtering and intelligent br T. Dumais. An analysis of information filtering methods. Communications
98.7 The Movable Filter as a User Interface Tool - Stone (1994)(Correct)
Magic Lens filters are a new user interface tool that combine an arbitrarily-shaped region with an operator that changes the view of objects viewed through that region. These tools can be interactivel... / alternate views and filtering information. Binding the filter to a
97.1 Implicit Rating and Filtering - Nichols (1998)(Correct)
Social filtering systems that use explicit ratings require a large number of ratings to remain viable. The effort involved for a user to rate a document may outweigh any benefit received, leading to a... / using implicit ratings for information filtering applications. br describe three forms of information filtering cognitive or content
97.1 Toward a Technology for Organizational Memories - Abecker, Bernardi, Hinkelmann, Kühn, .. (1998)(Correct)
To meet the growing need for enterprisewide knowledge management, the authors have developed and fielded a three-layered model for processing knowledge. This article shows how their organizational mem... / contrast to conventional information filtering-is primarily oriented br text summarization Information filtering Knowledge acquisition
95.6 Amalthaea: Information Discovery and Filtering using a Multiagent.. - Moukas (1996)(Correct)
Agents are semi-intelligent programs that assist the user in performing repetitive and time-consuming tasks. Information discovery and information filtering are a suitable domain for applying agent te... / Information discovery and information filtering are a suitable domain for br A co-evolution model of information filtering agents that adapt to the
93.6 An Introduction to Software Agents - Bradshaw (1997)(Correct)
ion and delegation: Agents can be made extensible and composable in ways that common iconic interface objects cannot. Because we can "communicate" with them, they can share our goals, rather than simp... / intelligent search and filtering information retrieval knowledge
90.9 WebSIFT: The Web Site Information Filter System - Cooley, Tan, Srivastava (1999)(Correct)
Web Usage Mining is the application of data mining techniques to large Web data repositories
in order to extract usage patterns. As with many data mining application domains,
the identification of p... / WebSIFT The Web Site Information Filter System Robert Cooley br and structure. The Web Site Information Filter WebSIFT system uses the
89.3 Interactive Assessment of User Preference Models: The Automated.. - Linden, Hanks, Lesh (1997)(Correct)
This paper presents the candidate/critique model of interactive problem solving,
in which an automated problem solver communicates candidate solutions to the user and
the user critiques those soluti... / support decision making and information filtering e.g. Thomas and Fischer br simple user model for information filtering and classification tasks
85.7 The SIFT Information Dissemination System - Yan (2000)(Correct)
Information dissemination is a powerful mechanism for finding information in wide-area environments.
An information dissemination server accepts long-term user queries, collects new documents from in... / of the Stanford Information Filtering Service SIFT a system br service the Stanford Information Filtering Tool SIFT SIFT was one
85.7 Learning to Recommend from Positive Evidence - Schwab, Pohl, Koychev (2000)(Correct)
In recent years, many systems and approaches for recommending
information, products or other objects have been developed. In
these systems, often machine learning methods that need training
input to a... / one hand content-based information filtering systems see for some
84.0 Bag of words and word psitions - Cohen (1995)(Correct)
Text categorization is the task of classifying text into one of several predefined categories. In this paper we will evaluate the effectiveness of several ILP methods for text categorization, and also... /
81.8 Information Foraging - Pirolli, Card (1999)(Correct)
Information Foraging Theory is an approach to understanding how strategies and
technologies for information seeking, gathering, and consumption are adapted to the flux
of information in the environmen... / with time allocation and information filtering and enrichment activities br up by the spiders' web. Information filtering systems Belkin Croft
81.8 Combining Content-Based and Collaborative Filters in an Online.. - Claypool, Gokhale, Miranda.. (1999)(Correct)
kkkkkkkkkkkkkk kkkkkkkkkkkkkcombines the coverage and speed of content-filters with the depth of collaborative filtering. We apply our research approach to an online newspaper, an as yet untapped oppo... / potential interest. We need information filters to help us prioritize news br sparse. Relatively dense information filtering domains will often still
81.8 Tailoring the Interaction With Users in Electronic Shops - Ardissono, Goy (1999)(Correct)
We describe the user modeling and personalization techniques adopted in SETA, a shell supporting the construction of adaptive Web stores which customize the interactions with users, suggesting the i... / to the applications in the information filtering area where several
81.1 Enhanced Dynamic Queries via Movable Filters - Fishkin, Stone (1995)(Correct)
Traditional database query systems allow users to construct
complicated database queries from specialized database
language primitives. While powerful and expressive, such
systems are not easy to use,... / graphical interface for filtering information. Magic Lens filters
74.2 On-line New Event Detection and Tracking - Allan, Papka, Lavrenko (1998)(Correct)
We define and describe the related problems
of new event detection and event tracking within a stream
of broadcast news stories. We focus on a strict on-line
setting---i.e., the system must make decis... / is similar to typical information filtering methods. We discuss the br have been proposed. Information Filtering systems are evaluated on a
72.7 A Maximum Likelihood Ratio Information Retrieval Model - Ng (1999)(Correct)
In this paper we present a novel probabilistic information retrieval
model that scores documents based on the relative change in the document
likelihoods, expressed as the ratio of the conditional pro... / applications such as information filtering where more relevance
72.7 Natural Language Processing and Information Retrieval - Voorhees (1999)(Correct)
Information retrieval addresses the problem of finding those
documents whose content matches a user's request from among a large
collection of documents. Currently, the most successful general pur... / encompassing such tasks as information filtering document summarization
72.3 Information Extraction: Techniques and Challenges - Grishman (1997)(Correct)
this paper
we shall use a narrower definition: the identification of instances of a particular
class of events or relationships in a natural language text, and the extraction of
the relevant arguments... / as any method for filtering information from large volumes of
71.4 Mining Navigation History for Recommendation - Fu (2000)(Correct)
Although a user's navigation history contains a lot of hidden information about the relationship between web pages and between users, this information is usually not exploited. The information hidden ... / method of collaborative information filtering. We implement a system br conventional collaborative information filtering method and methods based
69.5 A Case For Interaction: A Study Of Interactive Information Retrieval.. - Koenemann, Belkin (1996)(Correct)
This study investigates the use and effectiveness of
an advanced information retrieval (IR) system (INQUERY)
. 64 novice IR system users were studied in
their use of a baseline version of INQUERY comp... / Results in an information filtering task indicate that these br such situation is the information filtering or routing task in
68.5 Information Technology for Knowledge Management - Borghoff, Pareschi (1998)(Correct)
Knowledge has been lately recognized as one of the most important assets of
organizations. Can information technology help the growth and the sustainment of
organizational knowledge? The answer is y... / corporate memories information filtering Category A. H. .m br for IT integration. . Information Filtering The papers Profiling
68.5 A Bayesian framework for semantic content characterization - Vasconcelos, Lippman (1998)(Correct)
Current systems for content filtering, browsing, and
retrieval rely on low-level image descriptors which are
unintuitive for most users. In this paper, we propose
an alternative framework that exploit... / success for the problems of information filtering retrieval br solution to the problems of information filtering and retrieval and
66.6 Intelligent Adaptive Information Agents - Decker, Sycara (1996)(Correct)
Adaptation in open, multi-agent information gathering systems is important for several
reasons. These reasons include the inability to accurately predict future problem-solving
workloads, future cha... / in the retrieved information filtering away irrelevant or br themselves to gather and filter information in response to
66.6 Transportable Information Agents - Gray, Rus, Kotz (1996)(Correct)
We have designed and implemented autonomous software agents. Autonomous software
agents navigate independently through a heterogeneous network. They are capable of sensing
the network configuration, m... / in making decisions and filtering information. Transportable agents
66.6 Text Categorization and Relational Learning - Cohen (1995)(Correct)
We evaluate the first order learning system
FOIL on a series of text categorization problems.
It is shown that FOIL usually forms
classifiers with lower error rates and higher
rates of precision and r... / was learning to adapt an information filtering system to a single user's
66.6 Fast Incremental Indexing for Full-Text Information Retrieval - Brown, Callan, Croft (1994)(Correct)
Full-text information retrieval systems have traditionally been designed for archival environments. They often provide little or no support for adding new documents to an existing document collection,... / applications such as information filtering operate in dynamic br tion. Applications such as information filtering and daily news feed
63.8 Vapnik-Chervonenkis Dimension of Recurrent Neural Networks - Koiran (1997)(Correct)
Most of the work on the Vapnik-Chervonenkis dimension of neural networks has
been focused on feedforward networks. However, recurrent networks are also widely
used in learning applications, in particu... / systems for extracting information filtering of noise -from a
63.6 Agents that Buy and Sell: Transforming Commerce as we Know It - Maes, Guttman, Moukas (1999)(Correct)
Software agents have become very popular in the last six or so years. They have been
used successfully to filter information, match people with similar interests and automate
repetitive behavior. More... / behaviors involving information filtering and retrieval br and P. Maes. Social Information Filtering Algorithms for
63.6 Machine Learning in Automated Text Categorization - Sebastiani (1999)(Correct)
this paper concentrates on unknown Machine Learning in Automated
Fabrizio Sebastiani
Consiglio Nazionale delle Ricerche, Italy
Text Categorization
The automated categorization (or classification) o... / search and retrieval-Information filtering H. . Information br news. The construction of information filtering systems by means of ML
60.8 Incremental Relevance Feedback for Information Filtering - Allan (1996)(Correct)
We use data from the TREC routing experiments to explore how relevance feedback can be applied incrementally --- using a few judged documents each time --- to achieve results that are as good as if th... / Relevance Feedback for Information Filtering James Allan br Introduction An information filter monitors a stream of
59.5 Learning Routing Queries in a Query Zone - Singhal (1997)(Correct)
Word usage is domain dependent. A common word in one
domain can be quite infrequent in another. In this study we
exploit this property of word usage to improve document
routing. We show that routing q... / of information or information filtering. Most current state br relevance feedback for information filtering. In Proceedings of the
57.1 Ontology-Based Integration of Information - A Survey of Existing.. - Wache, Vögele, Visser.. (2001)(Correct)
We review the use on ontologies for the integration
of heterogeneous information sources. Based
on an in-depth evaluation of existing approaches to
this problem we discuss how ontologies are used t... / of information retrieval and information filtering Belkin and Croft br N.J. Belkin and B.W. Croft. Information filtering and information retrieval
57.1 Maximum Likelihood Estimation for Filtering Thresholds - Yi Zhang Jamie (2001)(Correct)
Information filtering systems based on statistical retrieval models
usually compute a numeric score indicating how well each
document matches each profile. Documents with scores above
profile-specific... / ABSTRACT Information filtering systems based on br the algorithm. Keywords Information Filtering Dissemination Threshold
57.1 Dynamics of an Information-Filtering Economy - Kephart, Hanson, Levine, Grosof.. (1998)(Correct)
Our overall goal is to characterize and understand the dynamic behavior of information economies: very large open economies of automated information agents that are likely to come into existence on ... / Dynamics of an Information-Filtering Economy Jeffrey O. br Here we model a simple information-filtering economy in which broker
55.0 Coordination Of Multiple Intelligent Software Agents - Sycara, Zeng (1996)(Correct)
this paper we present the distributed system architecture, agent collaboration interactions, and a reusable set of software components for structuring agents. The system architecture has three types o... / in the retrieved information filter away irrelevant or br whose main task is information filtering to alleviate the user's
54.5 Information-Theoretic Learning - Principe, Xu, III (1999)(Correct)
This chapter seeks to extend the ubiquitous mean-square error criterion (MSE) to cost functions
that include more information about the training data. Since the learning process ultimately
should tran... / ultimate goal of designing information filters We train learning br problem and also other information filtering problems such as pose
54.5 Agents in Delivering Personalized Content Based on Semantic Metadata - Kurki (1999)(Correct)
In the SmartPush project professional editors add semantic
metadata to information flow when the content is created.
This metadata is used to filter the information flow to
provide the end users wi... / in the delivery process. Information filtering is usually based either on br delivery. Metadata-based Information Filtering Textual information
51.4 An Interface for Learning Multi-topic User Profiles from Implicit.. - Balabanovic (1998)(Correct)
A text recommender system recommends sets of documents
for individual users on the basis of user models,
which are incrementally constructed given feedback
on previous recommendations. Users are reluc... / Its subsequent adoption by information filtering and recommender systems br is commonly observed in information filtering systems e.g.Sheth
51.4 A Customizable Coordination Service for Autonomous Agents - Singh (1998)(Correct)
We address the problem of constructing multiagent systems by coordinating
autonomous agents, whose internal designs may not be fully known. We
develop a customizable coordination service that (a) ... / Simple querying agent r Information filtering agent takes place in the
51.4 Implicit Feedback for Recommender Systems - Oard (1998)(Correct)
Can implicit feedback substitute for explicit ratings in recommender
systems? If so, we could avoid the difficulties
associated with gathering explicit ratings from users. How,
then, can we capture us... / their potential use for information filtering. Table presents the br M. and Shinoda Y. . Information Filtering Based on User Behavior
51.0 Software Agents: A review - Green, Hurst, Nangle, Cunningham.. (1997)(Correct)
this document.
5 unknown Software Agents: A review
27 May 1997
Trinity College Dublin
Broadcom Éireann Research Ltd.
Shaw Green
Leon Hurst
Brenda Nangle
Dr. Pádraig Cunningham
Fergal Somers
Dr. Ri... / . . Area Information Filtering Agents br of systems which perform an information filtering role some of which filter
51.0 Evolving a Multi-agent Information Filtering Solution in Amalthaea - Moukas, Zacharia (1997)(Correct)
Amalthaea is an evolving, multiagent ecosystem for
personalized filtering, discovery and monitoring of information
sites. Amalthaea's primary application domain
is the World-Wide-Web and its main purp... / Evolving a Multi-agent Information Filtering Solution in Amalthaea br Agents Evolution Information Filtering World-Wide-Web
45.7 Price-War Dynamics in a Free-Market Economy of Software Agents - Kephart, Hanson, Sairamesh (1998)(Correct)
One scenario of the future of computation populates the Internet with vast numbers of software agents providing, trading, and using a rich variety of information goods and services in an open, free-ma... / in the context of a simple information filtering economy. We consider only br Economy Our model of an information filtering economy consists of a
45.4 Machine Learning and Knowledge Representation in the LaboUr Approach.. - Wolfgang Pohl, Achim Nick (1999)(Correct)
In early user-adaptive systems, the use of knowledge representation methods for
user modeling has often been the focus of research. In recent years, however, the application
of machine learning te... / learning for personalized information filtering have been described in the br the user. In the case of information filtering systems however learning
42.8 Detecting Concept Drift with Support Vector Machines - Klinkenberg, Joachims (2000)(Correct)
For many learning tasks where data is collected over an extended period of time, its underlying distribution is likely to change. A typical example is information filtering, i.e. the adaptive classifi... / A typical example is information filtering i.e. the adaptive br The same problem occurs in information filtering i.e. the adaptive
42.8 Explaining Collaborative Filtering Recommendations - Joseph (2000)(Correct)
Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people... / traditional content-based information filtering system such as those br from that which is not. The information filter helps users to make
42.5 Experiences with GroupLens: Making Usenet Useful Again - Miller, Riedl, Konstan (1997)(Correct)
Collaborative filtering attempts to alleviate information
overload by offering recommendations
on whether information is valuable based
on the opinions of those who have already evaluated
it. Usenet n... / agents have been used in information filtering to prioritize messages br Intelligent agents for information filtering suffer from the same
40.5 ClockWorks: Visual Programming of Component-Based Software.. - Graham, Morton, Urnes (1996)(Correct)
ClockWorks is a programming environment supporting the visual programming of
object-oriented software architectures. In developing ClockWorks, we used user interface
evaluation techniques, including... / need for good support for information filtering and for easy refinement br it easy for the programmer to filter information. Relatedly in visual
40.0 A Transducer-Based XML Query Processor - Ludäscher, Mukhopadhyay.. (2002)(Correct)
The XML Stream Machine (XSM) system is a novel XQuery processing paradigm that is tuned to the efficient processing of sequentially accessed XML data (streams). The system compiles a given XQuery into... / e.g.efficient XML-based information filtering do not require the
40.0 Topic-Sensitive PageRank - Haveliwala (2002)(Correct)
In the original PageRank algorithm for improving the ranking
of search-query results, a single PageRank vector is computed,
using the link structure of the Web, to capture the
relative "importance" of... /
39.9 First-Order Learning for Web Mining - Craven (1998)(Correct)
We present compelling evidence that the World Wide Web is a domain in which applications can benefit from using first-order learning methods, since the graph structure inherent in hypertext naturall... / of applications including information filtering systems and browsing
36.3 User Modeling in Adaptive Interfaces - Langley (1999)(Correct)
In this paper we examine the notion of adaptive user interfaces, interactive systems that invoke machine learning to improve their interaction with humans. We review some previous work in this emerg... / on the generic task of information filtering which involves directing br systems. Systems for information filtering and recommendation are
35.0 The Effect of Multiple Query Representations on Information Retrieval .. - Belkin Cool School (1993)(Correct)
Five independently generated Boolean query formulations for ten
different TREC topics were produced by ten different expert
online searchers. These different formulations were grouped, and
the groups,... / retrieval performance in an information filtering environment which br N.J. CROFT W.B. Information filtering and information
34.7 Interface Agents that Learn: An Investigation of Learning Issues in a .. - Payne, Edwards (1995)(Correct)
In recent years, interface agents have been developed to assist users with various tasks. Some systems employ machine learning techniques to allow the agent to adapt to the user's changing requirement... / Mitchell et al. information filtering Sheth Payne br keywords for personalised information filtering. Other probablistic
34.7 Relevance Feedback With Too Much Data - James Allan Allan (1995)(Correct)
Modern text collections often contain large documents that span
several subject areas. Such documents are problematic for relevance
feedback since inappropriate terms can easily be chosen.
This study ... / the same techniques to the information filtering or routing environment
34.7 Learning from hotlists and coldlists: Towards a WWW information.. - Pazzani (1995)(Correct)
We describe a software agent that learns to find information on the World Wide Web (WWW),
deciding what new pages might interest a user. The agent maintains a separate hotlist (for links that
were int... / coldlists Towards a WWW information filtering and seeking agent
34.2 Learning While Filtering Documents - Callan (1998)(Correct)
This paper examines the problems of learning
queries and dissemination thresholds from relevance
feedback in a dynamic information filtering environment.
It revisits the EG algorithm for learning quer... / feedback in a dynamic information filtering environment. It revisits br in using it reliably for information filtering and providing solutions.
34.2 The Order of Things: Activity-Centred Information Access - Chalmers, Rodden, Brodbeck (1998)(Correct)
This paper focuses on the representation and access of Web-based information, and
how to make such a representation adapt to the activities or interests of individuals
within a community of users. The... / U. Maes P. Social Information Filtering Algorithms for Automating br indexing collaborative filtering information retrieval access and
34.2 Developing Formal Specifications to Coordinate Heterogeneous.. - Singh (1998)(Correct)
We have been developing an approach for the distributed
coordination of heterogeneous, autonomous agents. This
approach takes as input (a) agent skeletons, giving compact
descriptions of the given age... / . Example Skeleton for Information Filtering Example Figures and
34.2 Amalthaea: An Evolving Multi-Agent Information Filtering and.. - Moukas, Maes (1998)(Correct)
Amalthaea is an evolving, multiagent ecosystem for personalized filtering, discovery and
monitoring of information sites. Amalthaea's primary application domain is the WorldWide
-Web and its main pu... / An Evolving Multi-Agent Information Filtering and Discovery System for br Agents Evolution Information Filtering World-Wide-Web
34.0 Using a Semantic User Model to Filter the World Wide Web Proactively - Simons (1997)(Correct)
The research in this paper aims at using world knowledge to aid the user in
retrieving information from the World Wide Web. Some issues are identified together with
methods to address them.
1 Introd... / in a fixed archive. An information filtering system deals with a br develops a proactive information filter for the World Wide Web
31.8 A Conceptual Framework for Text Filtering - Oard, Marchionini (1996)(Correct)
This report develops a conceptual framework for text filtering practice and research,
and reviews present practice in the field. Text filtering is an information seeking process
in which documents are... / and specialized to define information filtering. The historical br Need Information Sources Information Filtering Stable and Specific
31.8 Autonomous and Adaptive Agents that Gather Information - Rus, Gray, Kotz (1996)(Correct)
We have designed and implemented autonomous software agents. Our agents are programs
that can move independently through a heterogeneous network of computers. They can sense
the state of the network, ... / in making decisions and filtering information. In this paper we discuss
31.8 Learning Probabilistic User Models - Billsus, Pazzani (1996)(Correct)
We describe two applications that use rated text documents to induce a model of the user's interests.
Based on our experiments with these applications we propose the use of a probabilistic learning
al... / in user modeling and information filtering tasks. Results from an br in user modeling and information filtering tasks. Results from an
31.8 Video query formulation - Ahanger (1995)(Correct)
For developing advanced query formulation methods for general multimedia data, we describe the issues related to
video data. We distinguish between the requirements for image retrieval and video retri... /
28.9 Using Agents to Improve the Usability and Usefulness of the.. - Thomas, Fischer (1996)(Correct)
The World-Wide Web (WWW) has emerged as a new type
of information space. Its lack of central control mechanisms
leads to many interesting new features but at the same time
has the potential danger tha... / Stevens as an information-filtering system that uses a br been named collaborative information filtering a technique to support
28.9 Multi-Agent Integration of Information Gathering and Decision Support - Sycara, Zeng (1996)(Correct)
We are investigating techniques for developing distributed and
adaptive collections of information agents that coordinate to retrieve,
filter and fuse information relevant to the user, task and sit... / whose main task is information filtering to alleviate the user's br accessing and filtering information about conference
28.9 Clustering and Information Sharing in an Ecology of Cooperating Agents - Foner (1995)(Correct)
Software agents have become increasingly popular
for a variety of applications, many of which benefit
from distribution on a network. However, many
common approaches scale poorly in such an environmen... / information discovery and information filtering in a networked br Shardanand Social Information Filtering Algorithms for Automating
28.9 Clustering and Information Sharing in an Ecology of Cooperating.. - Foner (1995)(Correct)
Many future applications for advanced software
agents imply distributed computation involving sensitive
or private data. Most efforts to date have assumed
that privacy may be traded away in order to
d... / Shardanand Social Information Filtering Algorithms for Automating
28.5 Using Labeled and Unlabeled Data to Learn Drifting Concepts - Klinkenberg (2001)(Correct)
For many learning tasks, where data is collected
over an extended period of time, one has to cope
two problems. The distribution underlying the data
is likely to change and only little labeled trai... / time. A typical example is information filtering i. e. the adaptive br The same problem occurs in information filtering i.e. the adaptive
28.5 Collaborative Learning for Recommender Systems - Lee (2001)(Correct)
Recommender systems use ratings from users on
items such as movies and music for the purpose
of predicting the user preferences on items that
have not been rated. Predictions are normally
done by ... / need for personalized information filtering systems. Recommender br Learning collaborative information filters. Proceedings of the
28.5 Eigentaste: A Constant Time Collaborative Filtering Algorithm - Goldberg, Roeder, Gupta, Perkins (2000)(Correct)
Eigentaste is a collaborative filtering algorithm that uses
universal queries to elicit real-valued user ratings on a common
set of items and applies principal component analysis
(PCA) to the resultin... / been proposed are social information filtering and recommender br Learning collaborative information filters. In AAAI Workshop on
28.5 Analyzing the Effectiveness and Applicability of Co-training - Nigam, Ghani (2000)(Correct)
Recently there has been significant interest in supervised
learning algorithms that combine labeled and unlabeled data
for text learning tasks. The co-training setting [1] applies to
datasets that hav... / Search and Retrieval-Information Filtering Keywords co-training
28.5 Expertise Recommender: A Flexible Recommendation System and.. - McDonald, Ackerman (2000)(Correct)
Locating the expertise necessary to solve difficult problems
is a nuanced social and collaborative problem. In organizations,
some people assist others in locating expertise by
making referrals. Peopl... / retrieval IR or information filtering. There are a wide variety br information retrieval and information filtering and it has great utility
28.5 Evaluation of Item-Based Top-N Recommendation Algorithms - Karypis (2000)(Correct)
The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of
recommender systems---a personalized information filtering technology used to identify a set o... / systems-a personalized information filtering technology used to br systems is a personalized information filtering technology used to either
28.5 Implicit Interest Indicators - Claypool, Le, Waseda, Brown (2000)(Correct)
Recommender systems provide personalized suggestions about
items that users will find interesting. Typically, recommender
systems require a user interface that can "intelligently
" determine the inte... / using implicit ratings for information filtering applications. He br on Machine Learning for Information Filtering . HG Daqing He
28.5 Personalized Conversational Case-Based Recommendation - Göker, Thompson (2000)(Correct)
In this paper, we describe the Adaptive Place Advisor, a user
adaptive, conversational recommendation system designed to help users decide
on a destination, specifically a restaurant. We view the ... / data processing level the information filtering level and the br adapts its behavior on the information filtering level and by changing the
27.2 A System For Automatic Personalized Tracking of Scientific Literature .. - Bollacker, Lawrence, Giles (1999)(Correct)
We introduce a system as part of the CiteSeer digital library
project for automatic tracking of scientific literature that is
relevant to a user's research interests. Unlike previous systems
that use ... / knowledge representation information filtering. INTRODUCTION There br that performs content based information filtering. There has been a great
27.2 Signature Caching Techniques for Information Filtering in Mobile.. - Lee (1999)(Correct)
This paper discusses signature caching strategies to reduce power consumption
for wireless broadcast and filtering services. The two-level signature scheme is
used for indexing the information frames.... / Caching Techniques for Information Filtering in Mobile Environments br stale. In order to support information filtering the signatures in the
27.2 Comprehension with[in] Virtual Environment Visualisations - Knight, Munro (1999)(Correct)
For many years basic visualisation, based around
simple boxes and lines, has been done in an attempt to
be able to ease some of the cognitive overload caused by
program comprehension. The problems wit... / pattern recognition information filtering coordination of multiple
27.2 Disseminating Mobile Agents for Distributed Information Filtering - Theilmann, Rothermel (1999)(Correct)
An often claimed benefit of mobile agent technology is
the reduction of communication cost. Especially the area of
information filtering has been proposed for the application
of mobile filter agents. ... / Agents for Distributed Information Filtering Wolfgang Theilmann Kurt br Especially the area of information filtering has been proposed for the
27.2 Machine Learning in Automated Text Categorisation - Sebastiani (1999)(Correct)
this paper. Aside from (i) the automatic assignment
of documents to a predefined set of categories, which is the main topic of
this paper, the term has also been used to mean (ii) the automatic defini... / searchand retrieval-Information filtering H. . Informati br news. The construction of information filteringsyerin by means of machine
27.2 Machine Learning in Automated Text Categorisation: a Bibliography - Sebastiani (1999)(Correct)
m, NL.
Yu, E. S. and Liddy, E. D. 1999. Feature selection in text categorization using the Baldwin
e#ect. In Proceedings of IJCNN-99, International Joint Conference on Neural Networks
(Washington, DC... / I. G. . Adaptive information filtering using evolutionary br I. G. . Adaptive information filtering algorithms. In D. J.
26.8 Design Issues for Virtual Reality Systems - Hubbold, Murta, West, Howard (1993)(Correct)
In this paper we describe a number of issues which are central to the design of a software architecture for a distributed, generic, virtual reality system. These include support for diverse and demand... / pattern recognition information filtering coordination of multiple
26.0 Machine Learning of User Profiles: Representational Issues - Bloedorn, Mani, MacMillan (1996)(Correct)
As more information becomes available electronically,
tools for finding information of interest to users becomes
increasingly important. The goal of the research
described here is to build a system fo... / for effective personalized information filters becomes critical. In br needs change over time. Information filtering as Belkin Croft
26.0 A Learning Agent that Assists the Browsing of Software Libraries - Drummond, Ionescu, Holte (1995)(Correct)
Locating software items is difficult, even for knowledgeable software designers, when searching
in large, complex and continuously growing libraries. This paper describes a technique,
we term active b... / describes a personalized information filtering agent which assists a br Agents For Personalized Information Filtering Proceeding of the Ninth
26.0 Artificial Life Applied to Adaptive Information Agents - Menczer (1995)(Correct)
We propose a model, inspired by recent artificial life theory, applied to the problem of retrieving information from a large, distributed collection of documents such as the World Wide Web. A populati... / Yang Korfhage and information filtering Maes Kozierok br Agents for Personalized Information Filtering. Proc. th IEEE
25.5 A Multilevel Approach to Intelligent Information Filtering: Model.. - Mostafa (1997)(Correct)
this article, a filtering model is proposed that decomposes the overall task into subsystem functionalities and highlights the need for multiple adaptation techniques to cope with uncertainties. A fil... / Approach to Intelligent Information Filtering Model System and br Purdue University In information-filtering environments
24.6 Distributed Selective Dissemination of Information - Yan (1994)(Correct)
To help users cope with information overload, Selective Dissemination of Information (SDI) will increasingly become an important tool in wide area information systems. In an SDI service, users post th... / provide such kind of information filtering service named br LT S. Loeb and D. Terry. Information filtering. Communication of the
23.1 Experience with Learning Agents which Manage Internet-Based.. - Edwards (1996)(Correct)
To provide assistance with tasks such as retrieving USENET news articles or identifying interesting Web pages, an intelligent agent requires information about a user's interests and needs. Machine lea... / personalised tasks such as information filtering. An alternative solution br learning techniques with information filtering techniques to create user
23.1 Domain and Language Independent Feature Extraction for Statistical.. - Bayer, Renz, Stein, Kressel (1996)(Correct)
A generic system for text categorization is presented which uses a representative text corpus to
adapt the processing steps: feature extraction, dimension reduction, and classification. Feature
extrac... / categorization systems are information filtering and information br systems are information filtering and information retrieval. Furthermore
23.1 Join Queries with External Text Sources: Execution and Optimization.. - Chaudhuri (1995)(Correct)
Text is a pervasive information type, and many applications require querying over text sources in addition to structured data. This paper studies the problem of query processing in a system that loose... / or a phrase e.g.information filtering'When there are more br or and not e.g.information filtering' and AU smith'
22.8 Multi-agent Coordination through Coalition Formation - Shehory, Sycara, Jha (1998)(Correct)
Incorporating coalition formation algorithms into agent systems
shall be advantageous due to the consequent increase in the overall
quality of task performance. Coalition formation was addressed
in ga... / whose main task is information filtering to alleviate the user's
22.8 Foresight-based pricing algorithms in an economy of software agents - Tesauro, Kephart (1998)(Correct)
We propose several heuristic approaches to the development
of pricing algorithms for software agents that incorporate
foresight, i.e., an ability to model and predict responses by
competitors. In the ... / information sellers in an information filtering economy but they can br that we have studied is an informationfiltering model described in detail
22.8 A Comparison of Indexing Methods for Data Broadcast on the Air - Qinglong Hu (1998)(Correct)
Several indexing techniques for data broadcast on the
air have been proposed for power conservation on mobile
computers in the past few years. Indexing techniques for
broadcast channels can save batte... / following advantages for information filtering ffl They can improve br signature techniques for information filtering in wireless and mobile
22.2 Social Information Filtering for Music Recommendation - Shardanand (1994)(Correct)
Filters which select items for individual users based upon content suffer from several
limitations. The items being filtered must be amenable to parsing by a computer.
Furthermore, Content-Based Filte... / Social Information Filtering for Music Recommendation br Theses Social Information Filtering for Music Recommendation
21.2 Using Grammatical Inference to Improve Precision in Information.. - Freitag (1997)(Correct)
The field of information extraction (IE) is concerned
with applying natural language processing (NLP) and
information retrieval (IR) techniques to the automatic
extraction of essential details from te... / information retrieval and information filtering have finally come into
21.0 Information Filtering: Selection Mechanisms In Learning Systems - Markovitch (1989)(Correct)
interpreter for logic programs (Sterling & Shapiro, 1986)...................138
1
1. INTRODUCTION
The most important outcome of AI research during the 70s was the
general acceptance of the major rol... / Information Filtering Selection Mechanisms In br . . Types of information
18.1 Information Filtering in Changing Domains - Lanquillon (1999)(Correct)
The task of information filtering is to classify
documents from a stream into either relevant
or irrelevant according to a particular user interest
with the objective to reduce information
load. When ... / Information Filtering in Changing Domains br Abstract The task of information filtering is to classify documents
18.1 Alipes: A Swift Messenger in Cyberspace - Widyantoro, Yin, Nasr, Yang, Zacchi, .. (1999)(Correct)
Finding relevant information effectively on the Internet
is a challenging task. Although the information
is widely available, exploring Web sites
and selecting the right document are still considered
... / ecosystem of evolving information-filtering and discovery agents that br documents on the next information filtering session will be scored
18.1 Adaptive Information Filtering: improvement of the matching technique .. - Tauritz (1999)(Correct)
Adaptive Information Filtering is concerned with filtering information streams
in changing environments. The changes may occur both on the transmission
side (the nature of the streams can change) and... / Adaptive Information Filtering improvement of the br Abstract Adaptive Information Filtering is concerned with
18.1 Architecture-Based Specification-Time Software Evolution - Medvidovic (1999)(Correct)
OF THE DISSERTATION
Architecture-Based
Specification-Time Software Evolution
by
Nenad Medvidovic
Doctor of Philosophy in Information and Computer Science
University of California, Irvine, 1999
Profes... / and heterogeneous information filtering mechanisms and br this aspect of interaction. Information filtering constitutes a spectrum
17.3 Toward Interaction-Oriented Programming - Singh (1996)(Correct)
Although much progress has been made in agent theory and practice, bottlenecks remain in the construction of complex multiagent systems. We introduce interaction-oriented programming (IOP) as an appro... / information retrieval information filtering querying heterogeneous br Figure Skeleton for an information filtering agent The manager is not
17.3 Representational Issues in Machine Learning of User Profiles - Bloedorn, Mani, MacMillan (1996)(Correct)
As more information becomes available electronically, tools for finding information of interest to users become increasingly important. Building tools for assisting users in finding relevant informati... / for effective personalized information filters becomes critical. In br needs change over time. Information filtering as Belkin and Croft