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Predicting category accesses for a user in a structured information space
- In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
, 2002
"... In a categorized information space, predicting users ’ information needs at the category level can facilitate personalization, caching and other topic-oriented services. This paper presents a two-phase model to predict the category of a user’s next access based on previous accesses. Phase 1 generate ..."
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Cited by 14 (1 self)
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In a categorized information space, predicting users ’ information needs at the category level can facilitate personalization, caching and other topic-oriented services. This paper presents a two-phase model to predict the category of a user’s next access based on previous accesses. Phase 1 generates a snapshot of a user’s preferences among categories based on a temporal and frequency analysis of the user’s access history. Phase 2 uses the computed preferences to make predictions at different category granularities. Several alternatives for each phase are evaluated, using the rating behaviors of on-line raters as the form of access considered. The results show that a method based on re-access pattern and frequency analysis of a user’s whole history has the best prediction quality, even over a path-based method (Markov model) that uses the combined history of all users.
The Design and Evaluation of Web Prefetching and Caching Techniques
, 2002
"... User-perceived retrieval latencies in the World Wide Web can be improved by pre-loading a local cache with resources likely to be accessed. A user requesting content that can be served by the cache is able to avoid the delays inherent in the Web, such as congested networks and slow servers. The diff ..."
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Cited by 13 (2 self)
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User-perceived retrieval latencies in the World Wide Web can be improved by pre-loading a local cache with resources likely to be accessed. A user requesting content that can be served by the cache is able to avoid the delays inherent in the Web, such as congested networks and slow servers. The difficulty, then, is to determine what content to prefetch into the cache. This work explores machine learning algorithms for user sequence prediction, both in general and specifically for sequences of Web requests. We also consider information retrieval techniques to allow the use of the content of Web pages to help predict future requests. Although history-based mechanisms can provide strong performance in predicting future requests, performance can be improved by including predictions from additional sources. While past researchers have used a variety of techniques for evaluating caching algorithms and systems, most of those methods were not applicable to the evaluation of prefetching algorithms or systems. Therefore, two new mechanisms for evaluation are introduced. The first is a detailed trace-based simulator, built from scratch,
Learning Web Request Patterns
- Web Dynamics: Adapting to Change in Content, Size, Topology and Use
, 2004
"... Summary. Most requests on the Web are made on behalf of human users, and like other human-computer interactions, the actions of the user can be characterized by identifiable regularities. Much of these patterns of activity, both within a user, and between users, can be identified and exploited by in ..."
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Cited by 11 (1 self)
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Summary. Most requests on the Web are made on behalf of human users, and like other human-computer interactions, the actions of the user can be characterized by identifiable regularities. Much of these patterns of activity, both within a user, and between users, can be identified and exploited by intelligent mechanisms for learning Web request patterns. Our focus is on Markov-based probabilistic techniques, both for their predictive power and their popularity in Web modeling and other domains. Although history-based mechanisms can provide strong performance in predicting future requests, performance can be improved by including predictions from additional sources. In this chapter we review the common approaches to learning and predicting Web request patterns. We provide a consistent description of various algorithms (often independently proposed), and compare performance of those techniques on the same data sets. We also discuss concerns for accurate and realistic evaluation of these techniques. 1
On the Use of Constrained Associations for Web Log Mining
, 2002
"... this paper, we first present an approach based on association rule mining. Our algorithm discovers association rules that are constrained (and ordered) temporally. The approach relies on the simple premise that pages accessed recently have a greater influence on pages that will be accessed in the ne ..."
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Cited by 8 (0 self)
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this paper, we first present an approach based on association rule mining. Our algorithm discovers association rules that are constrained (and ordered) temporally. The approach relies on the simple premise that pages accessed recently have a greater influence on pages that will be accessed in the near future. The approach not only results in better predictions, it also prunes the rule-space significantly, thus enabling faster online prediction. Further refinements based on sequential dominance are also evaluated, and prove to be quite effective. Detailed experimental evaluation shows how the approach is quite effective in capturing a web user's access patterns; consequently, our prediction model not only has good prediction accuracy, but also is more efficient in terms of space and time complexity. The approach is also likely to generalize for e-commerce recommendation systems
COWES: Clustering Web Users Based on Historical Web Sessions
"... Abstract. Clustering web users is one of the most important research topics in web usage mining. Existing approaches cluster web users based on the snapshots of web user sessions. They do not take into account the dynamic nature of web usage data. In this paper, we focus on discovering novel knowled ..."
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Abstract. Clustering web users is one of the most important research topics in web usage mining. Existing approaches cluster web users based on the snapshots of web user sessions. They do not take into account the dynamic nature of web usage data. In this paper, we focus on discovering novel knowledge by clustering web users based on the evolutions of their historical web sessions. We present an algorithm called COWES to cluster web users in three steps. First, given a set of web users, we mine the history of their web sessions to extract interesting patterns that capture the characteristics of their usage data evolution. Then, the similarity between web users is computed based on their common interesting patterns. Then, the desired clusters are generated by a partitioning clustering technique. Web user clusters generated based on their historical web sessions are useful in intelligent web advertisement and web caching. 1
An open framework for smart and personalized distance learning
- 1st International Conference on Advances in Web-Based Learning, Hong Kong
, 2002
"... Abstract. Web based learning enables more students to have access to the distance-learning environment and provides students and teachers with unprecedented flexibility and convenience. However, the early experience of using this new learning means in China exposes a few problems. Among others, teac ..."
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Abstract. Web based learning enables more students to have access to the distance-learning environment and provides students and teachers with unprecedented flexibility and convenience. However, the early experience of using this new learning means in China exposes a few problems. Among others, teachers accustomed to traditional teaching methods often find it difficult to put their courses online and some students, especially the adult students, find themselves overloaded with too much information. In this paper, we present an open framework to solve these two problems. This framework allows students to interact with an automated question answering system to get their answers. It enables teachers to analyze students learning patterns and organize the webbased contents efficiently. The framework is intelligent due to the data mining and case-based reasoning features, and user-friendly because of its personalized services to both teachers and students. 1
Construction and Application of the Learning Behavior Analysis Center based on Open E-Learning Platform
"... Abstract. Nowadays, the most prevailing issue in the e-learning environment is that it is not easy to monitor students ’ learning behaviors instantly, which is becoming the mainly obstacle of personalized teaching and learning. For example, the teacher is confused--- know nothing about the learning ..."
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Abstract. Nowadays, the most prevailing issue in the e-learning environment is that it is not easy to monitor students ’ learning behaviors instantly, which is becoming the mainly obstacle of personalized teaching and learning. For example, the teacher is confused--- know nothing about the learning status of students, the content is static--- invariable and same to every student and the student is alone--- can not know the learning status of himself and can’t share the experience of other students. In this paper, we construct a learning behavior analysis and monitor system based on open e-learning platform that solves these problems. It includes three main modules, the Uniform Data Specification Module, the Data Mining Module and the Data Visualization Module, which can integrate and pretreat the learning historical data, mining the learning patterns and learning status of students. Specially, the user-friendly visualization interface can help the teacher to analyze students ’ learning patterns, adjust the teaching plan and organize the web-based contents efficiently.
Restoring Meaningful Episodes in a Proxy Log
"... Web logs collected at proxy servers, referred to as proxy logs, contain rich information about Web user activities. These logs are becoming critical data sources for various Web applications such as Web log mining. However, a raw proxy log treated as a flat sequence of individual Web requests doe ..."
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Web logs collected at proxy servers, referred to as proxy logs, contain rich information about Web user activities. These logs are becoming critical data sources for various Web applications such as Web log mining. However, a raw proxy log treated as a flat sequence of individual Web requests does not reliably represent correct information about Web user behavior, owing to a lack of semantic structure. This problem has consequently impaired Web mining results from discovering meaningful knowledge e#ectively. The W3C WCA working group conceptualizes the online behavior of a Web user in terms of user sessions and episodes. A user session is a delimited set of user clicks across one or more Web servers. An episode is a subset of related user clicks in a user session. In this paper, we investigate the problem of restoring semantic structure in a proxy log by classifying individual Web requests into semantically meaningful episodes so that it can serve as a more reliable input than its raw format for various knowledge discovery processes. Existing approaches to transaction identification as well as other log preprocessing techniques are found inadequate to identify episodes with clear semantics from proxy logs.
Cleopatra: Evolutionary Pattern-based Clustering of Web Usage Data
"... Abstract. Existing web usage mining techniques focus only on discovering knowledge based on the statistical measures obtained from the static characteristics of web usage data. They do not consider the dynamic nature of web usage data. In this paper, we present an algorithm called Cleopatra (CLuster ..."
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Abstract. Existing web usage mining techniques focus only on discovering knowledge based on the statistical measures obtained from the static characteristics of web usage data. They do not consider the dynamic nature of web usage data. In this paper, we present an algorithm called Cleopatra (CLustering of EvOlutionary PAtTeRn-based web Access sequences) to cluster web access sequences (WASs) based on their evolutionary patterns. In this approach, Web access sequences that have similar change patterns in their support counts in the history are grouped into the same cluster. The intuition is that often WASs are event/task-driven. As a result, WASs related to the same event/task are expected to be accessed in similar ways over time. Such clusters are useful for several applications such as intelligent web site maintenance and personalized web services. 1
A New Method to Create the Profile and Improving the Queries in Web
"... Finding needed information among the existing information on the web can be very time consuming and difficult. To tackle this problem, web personalization systems have been proposed that adapt the contents and services of web sites based on the users ’ interests. Studying users ’ behaviors in the pa ..."
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Finding needed information among the existing information on the web can be very time consuming and difficult. To tackle this problem, web personalization systems have been proposed that adapt the contents and services of web sites based on the users ’ interests. Studying users ’ behaviors in the past with web usage mining techniques utilization can be worthy help in personalization affair. Web servers log files considered as a rich resource for finding users ’ behavioral patterns. In this paper, users ’ behavioral patterns are obtained from studying of users ’ access and web usage mining utilization, especially users clustering. One of the innovative aspects of the research is selecting some behavioral features from users. These features include the ‘pages view’, ’page view frequency’, ‘time period of viewing the pages ’ and ‘ order of viewing the pages ’ which are stored in users ’ profiles. In addition is considered weight criterion for the first three features. Thus clustering has been done by considering this criterion with K-means algorithm. Neural network usage is another feature of proposed system to form recommender engine, which its function is to find proper behavioral pattern for users ’ session and forecast upcoming demands. As research conclusion presents recommender engine has the appropriate accuracy in prediction of user’s inquiry.
