Integrating collaborative filtering and search-based techniques for personalized online product recommendation
Abdullah, Noraswaliza, Xu, Yue, & Geva, Shlomo (2011) Integrating collaborative filtering and search-based techniques for personalized online product recommendation. In Spiliopoulou, Myra, Wang, Haixun, Cook, Diane, Pei, Jian, Wang, Wei, Zaiane, Osmar, et al. (Eds.) Proceedings of the 4th Workshop on Data Mining Case Studies (IEEE ICDM2011), IEEE Computer Society Conference Publications, Marriott Pinnacle Downtown, Vancouver, pp. 711-718.
The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not frequently purchased by users, such as cars and houses, as it is difficult to collect rating data for such products from the users. Many of the e-commerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended to the user. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their online navigation behaviour. This paper proposes to integrate collaborative filtering and search-based techniques to provide personalized recommendations for infrequently purchased products. Two different techniques are proposed, namely CFRRobin and CFAg Query. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAg Query technique uses the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAg Query perform better than the standard Collaborative Filtering (CF) and the Basic Search (BS) approaches, which are widely applied by the current e-commerce applications. The CFRRobin and CFAg Query approaches also outperform the e- isting query expansion (QE) technique that was proposed for recommending infrequently purchased products.
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|Item Type:||Conference Paper|
|Keywords:||Recommender system, Collaborative filtering, Search-based technique, Personalization|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > DISTRIBUTED COMPUTING (080500) > Web Technologies (excl. Web Search) (080505)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright © 2011 by IEEE|
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|Deposited On:||01 Feb 2012 22:18|
|Last Modified:||17 Mar 2012 10:51|
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