Proceedings ArticleDOI
Effective personalization based on association rule discovery from web usage data
Bamshad Mobasher,Honghua Dai,Tao Luo,Miki Nakagawa +3 more
- Vol. 3, pp 9-15
TLDR
This paper proposes effective and scalable techniques for Web personalization based on association rule discovery from usage data that can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy.Abstract:
To engage visitors to a Web site at a very early stage (i.e., before registration or authentication), personalization tools must rely primarily on clickstream data captured in Web server logs. The lack of explicit user ratings as well as the sparse nature and the large volume of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance. Web usage mining techniques such as clustering that rely on offline pattern discovery from user transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of reduced recommendation accuracy. In this paper we propose effective and scalable techniques for Web personalization based on association rule discovery from usage data. Through detailed experimental evaluation on real usage data, we show that the proposed methodology can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy.read more
Citations
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Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales
TL;DR: Krishnan et al. as mentioned in this paper investigated the long tail phenomenon of the Pareto principle and found that consumers' usage of Internet search and discovery tools, such as recommendation engines, is associated with an increase in the share of niche products.
Journal ArticleDOI
Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales
TL;DR: Krishnan et al. as mentioned in this paper investigated the long tail phenomenon of the Pareto principle and found that consumers' usage of Internet search and discovery tools, such as recommendation engines, is associated with an increase in the share of niche products.
Journal ArticleDOI
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
TL;DR: This study shows that both user-based and item-based algorithms are highly vulnerable to specific attack models, but that hybrid algorithms may provide a higher degree of robustness.
Journal ArticleDOI
Mining interesting knowledge from weblogs: a survey
TL;DR: A survey of the recent developments in Web Usage Mining, that area of Web Mining which deals with the extraction of interesting knowledge from logging information produced by Web servers, is presented.
References
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Proceedings Article
Fast algorithms for mining association rules
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
Proceedings Article
Fast Algorithms for Mining Association Rules in Large Databases
Proceedings ArticleDOI
Social information filtering: algorithms for automating “word of mouth”
Upendra Shardanand,Pattie Maes +1 more
TL;DR: The implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists, and four different algorithms for making recommendations by using social information filtering were tested and compared.
Journal ArticleDOI
Web usage mining: discovery and applications of usage patterns from Web data
TL;DR: Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications as mentioned in this paper, where preprocessing, pattern discovery, and pattern analysis are described in detail.
Proceedings ArticleDOI
Analysis of recommendation algorithms for e-commerce
TL;DR: This paper investigates several te hniques for analyzing large-s ale pur hase and preferen e data for the purpose of producing useful re ommendations to ustomers and devise and apply their ombinations on the authors' data sets to ompare for re Ommendation quality and performan e.