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166
Effective personalization based on association rule discovery from web usage data. In:
- Proceedings of the 3rd International Workshop on Web Information and Data Management,
, 2001
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A Data Mining Algorithm for Generalized Web Prefetching
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2003
"... Predictive Web prefetching refers to the mechanism of deducing the forthcoming page accesses of a client based on its past accesses. In this paper, we present a new context for the interpretation of Web prefetching algorithms as Markov predictors. We identify the factors that affect the performanc ..."
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Cited by 76 (16 self)
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Predictive Web prefetching refers to the mechanism of deducing the forthcoming page accesses of a client based on its past accesses. In this paper, we present a new context for the interpretation of Web prefetching algorithms as Markov predictors. We identify the factors that affect the performance of Web prefetching algorithms. We propose a new algorithm called WM o , which is based on data mining and is proven to be a generalization of existing ones. It was designed to address their specific limitations and its characteristics include all the above factors. It compares favorably with previously proposed algorithms. Further, the algorithm efficiently addresses the increased number of candidates. We present a detailed performance evaluation of WM o with synthetic and real data. The experimental results show that WM o can provide significant improvements over previously proposed Web prefetching algorithms.
Model-based clustering and visualization of navigation patterns on a web site
- Data Mining and Knowledge Discovery
, 2003
"... We present a new methodology for exploring and analyzing navigation patterns on a web site. The patterns that can be analyzed consist of sequences of URL categories traversed by users. In our approach, we rst partition site users into clusters such that users with similar navigation paths through th ..."
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Cited by 74 (0 self)
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We present a new methodology for exploring and analyzing navigation patterns on a web site. The patterns that can be analyzed consist of sequences of URL categories traversed by users. In our approach, we rst partition site users into clusters such that users with similar navigation paths through the site are placed into the same cluster. Then, for each cluster, we display these paths for users within that cluster. The clustering approach weemployis model-based (as opposed to distance-based) and partitions users according to the order in which they request web pages. In particular, we cluster users by learning a mixture of rst-order Markov models using the Expectation-Maximization algorithm. The runtime of our algorithm scales linearly with the number of clusters and with the size of the data � and our implementation easily handles hundreds of thousands of user sessions in memory. In the paper, we describe the details of our method and a visualization tool based on it called WebCANVAS. We illustrate the use of our approach on user-tra c data from msnbc.com. Keywords: Model-based clustering, sequence clustering, data visualization, Internet, web 1
Intelligent Techniques for Web Personalization”
- in post-proceedings of the Second Workshop on Intelligent Techniques in Web Personalization,
, 2005
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Using Ontologies to Discover Domain-Level Web Usage Profiles
, 2002
"... Usage patterns discovered through Web usage mining are effective in capturing item-to-item and user-to-user relationships and similarities at the level of user sessions Without the benefit of deeper domain knowledge, such patterns provide little insight into the underlying reasons for which such ite ..."
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Cited by 46 (7 self)
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Usage patterns discovered through Web usage mining are effective in capturing item-to-item and user-to-user relationships and similarities at the level of user sessions Without the benefit of deeper domain knowledge, such patterns provide little insight into the underlying reasons for which such items or users are grouped together This can lead to a number of important shortcomings in personalization systems based on Web usage mining or collaborative filtering. For example, if a new item is recently added to the Web site, it is not likely that the pages associated with the item would be a part of any of the discovered patterns, and thus these pages cannot be recommended. Keyword-based content-filtering approaches have been used to enhance the effectiveness of collaborative filtering systems by focusing on content similarity among items or pages. These approaches, however, are incapable of capturing more complex relationships at a deeper semantic level based on different types of attributes associated with structured objects. This paper represents work-in-progress towards creating a general framework for using domain ontologies to automatically characterize usage profiles containing a set of structured Web objects. Our motivation is to use this framework in the context of Web personalization, going beyond page- or item-level constructs, and using the full semantic power of the underlying ontology.
A hybrid web personalization model based on site connectivity.
- Proceedings of WebKDD,
, 2003
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Effective Prediction of Web-user Accesses: A Data Mining Approach
, 2001
"... The problem of predicting web-user accesses has recently attracted significant attention. Several algorithms have been proposed, which find important applications, like user profiling, recommender systems, web prefetching, design of adaptive web sites, etc. In all these applications the core issue i ..."
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Cited by 38 (1 self)
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The problem of predicting web-user accesses has recently attracted significant attention. Several algorithms have been proposed, which find important applications, like user profiling, recommender systems, web prefetching, design of adaptive web sites, etc. In all these applications the core issue is the developement of an e#ective prediction algorithm. In this paper, we focus on web-prefetching, because of its importance in reducing user perceived latency present in every Web-based application. The proposed method can be easily extended to the other aforementioned applications.
Modeling user search behavior
- in LA-WEB ’05: Proceedings of the Third Latin American Web Congress
"... Web usage mining is a main research area in Web min-ing focused on learning about Web users and their interac-tions with Web sites. Main challenges in Web usage mining are the application of data mining techniques to Web data in an efficient way and the discovery of non trivial user behav-iour patte ..."
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Cited by 27 (4 self)
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Web usage mining is a main research area in Web min-ing focused on learning about Web users and their interac-tions with Web sites. Main challenges in Web usage mining are the application of data mining techniques to Web data in an efficient way and the discovery of non trivial user behav-iour patterns. In this paper we focus the attention on search engines analyzing query log data and showing several mod-els about how users search and how users use search engine results. 1.
