期刊名称：Journal of Theoretical and Applied Electronic Commerce Research
印刷版ISSN：0718-1876
电子版ISSN：0718-1876
出版年度：2013
卷号：8
期号：2
页码：34-48
语种：English
出版社：Universidad de Talca
其他摘要：Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current reco mmender systems are developed for simple and frequently purchased products like books and vi deos, by using collaborative-filt ering and content-based approaches. These approaches are not directly applicable for reco mmending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the e- commerce sites for infrequently purchased products are st ill using basic search-bas ed techniques whereby the products that match with the attr ibutes given in the target user ’s query are retrieved and recommended. However, search-based recommenders cannot provide pers onalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user prof iling approach is proposed to generate user’s preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similar- minded users (i.e., neighbours) accurately. Two re commendation approaches are proposed, namely Round- Robin fusion algorithm (CFRRobin) and Collaborat ive Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user’s query to search for products as norma l search based systems do, the CFRRobin technique uses the attributes of the products in which the target us er’s neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target us er a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of 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 CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are wi dely applied by the current e-commerce applications.
关键词：Collaborative filtering; Recommend er systems; Product search; Product recommendation; Personalization; User profiling
