Personalized information delivery: an analysis of information filtering methods
Communications of the ACM - Special issue on information filtering
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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Retrieving relevant items which meet a user's information need is the key objective of information retrieval (IR). Current IR systems generally seek to satisfy search queries independently without considering search history information from other searchers. By contrast, algorithms used in recommender systems (RSs) are designed to predict the future popularity of an item by aggregating ratings of the reactions of previous users of an item. This observation motivates us to explore the application of RS methods in IR to increase search effectiveness. In this study, we examine the suitability of recommender algorithms (RAs) for use in IR applications and methods for combining RAs into IR systems by fusing their respective outputs. A novel RA is proposed to enhance the RS performance in our integrated application. Experimental results are reported for an extended version of the FIRE 2011 personalized IR data collection. Noticeably better results are obtained using our approach.