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Communications of the ACM
Dynamic itemset counting and implication rules for market basket data
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Mining navigation history for recommendation
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Efficient search for association rules
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ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
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ACM Transactions on Information Systems (TOIS)
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Collaborative Filtering Using a Regression-Based Approach
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Applied Intelligence
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FS'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Fuzzy Systems - Volume 8
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MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
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Expert Systems with Applications: An International Journal
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WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Algorithm of mining sequential patterns for web personalization services
ACM SIGMIS Database
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Expert Systems with Applications: An International Journal
Web Semantics: Science, Services and Agents on the World Wide Web
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The Journal of Machine Learning Research
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Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among customers' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few rules; this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm specifically designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be specified in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a specific target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is significantly better than that of traditional correlation-based approaches.