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1,159
The structure and function of complex networks
- SIAM REVIEW
, 2003
"... Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, ..."
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Cited by 2600 (7 self)
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Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2005
"... This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes vario ..."
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Cited by 1490 (23 self)
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This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
Item-based Collaborative Filtering Recommendation Algorithms
- PROC. 10TH INTERNATIONAL CONFERENCE ON THE WORLD WIDE WEB
, 2001
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Evaluating collaborative filtering recommender systems
- ACM TRANSACTIONS ON INFORMATION SYSTEMS
, 2004
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Grouplens: Applying collaborative filtering to usenet news
- COMMUNICATIONS OF THE ACM
, 1997
"... ... a collaborative filtering system for Usenet news—a high-volume, high-turnover discussion list service on the Internet. Usenet newsgroups—the individual discussion lists—may carry hundreds of messages each day. While in theory the newsgroup organization allows readers to select the content that m ..."
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Cited by 803 (18 self)
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... a collaborative filtering system for Usenet news—a high-volume, high-turnover discussion list service on the Internet. Usenet newsgroups—the individual discussion lists—may carry hundreds of messages each day. While in theory the newsgroup organization allows readers to select the content that most interests them, in practice most newsgroups carry a wide enough spread of messages to make most individuals consider Usenet news to be a high noise information resource. Furthermore, each user values a different set of messages. Both taste and prior knowledge are major factors in evaluating news articles. For example, readers of the rec.humor newsgroup, a group designed for jokes and other humorous postings, value articles based on whether they perceive them to be funny. Readers of technical groups, such as comp.lang.c� � value articles based
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
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Cited by 727 (18 self)
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The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting algorithm for combining preferences called RankBoost. We also describe an efficient implementation of the algorithm for certain natural cases. We discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different WWW search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. Here, we present results comparing RankBoost to nearest-neighbor and regression algorithms.
SimRank: A Measure of Structural-Context Similarity
- In KDD
, 2002
"... The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to- ..."
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Cited by 387 (3 self)
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The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects." This general similarity measure, called SimRank, is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.
Kasbah: an agent marketplace for buying and selling goods,
- Proceedings of the 1st International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology,
, 1996
"... Abstract While there are many Web services which help users find things to buy, we know of none which actually try to automate the process of buying and selling. Kasbah is a system where users create autonomous agents to buy and sell goods on their behalf. In this paper, we describe how Kasbah work ..."
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Cited by 386 (6 self)
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Abstract While there are many Web services which help users find things to buy, we know of none which actually try to automate the process of buying and selling. Kasbah is a system where users create autonomous agents to buy and sell goods on their behalf. In this paper, we describe how Kasbah works. We also discuss the implementation of a simple proof-of-concept prototype.
