Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
A semisupervised learning method to merge search engine results
ACM Transactions on Information Systems (TOIS)
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Nowadays, Collaborative Filtering (CF) Recommender Systems have been widely applied by commercial e-commerce sites for recommending simple and frequently purchased products to users. The existing CF technique is not directly applicable for recommending products that are not regularly purchased by the users because it is difficult to collect a large amount of ratings or previous purchased history data for this kind of product. This paper proposes to integrate collaborative filtering and search-based techniques for recommending these products. Instead of directly recommending products that the user's neighbors have an interest in to the active user, the proposed technique, named CFRRobin, uses the products as queries to retrieve other relevant products. Then the returned products from all the queries are merged and ranked by using the Round-Robin method, in order to select the final products to recommend. Experiments conducted on real e-commerce data show that the proposed approach outperforms the Basic Search (BS) and the standard Collaborative Filtering (CF Original) approaches, which are widely applied by the current e-commerce applications. The CFRRobin technique also performs better than the Query Expansion (QE) approach that has been proposed for recommending infrequently purchased products.