Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Clustering e-commerce search engines
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Webpage understanding: beyond page-level search
ACM SIGMOD Record
Towards a theory model for product search
Proceedings of the 20th international conference on World wide web
Diversifying product search results
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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With the rapid growth of E-Commerce on the Internet, online product search service has emerged as a popular and effective paradigm for customers to find desired products and select transactions. Most product search engines today are based on adaptations of relevance models devised for information retrieval. However, there is still a big gap between the mechanism of finding products that customers really desire to purchase and that of retrieving products of high relevance to customers' query. In this paper, we address this problem by proposing a new ranking framework for enhancing product search based on dynamic best-selling prediction in E-Commerce. Specifically, we first develop an effective algorithm to predict the dynamic best-selling, i.e. the volume of sales, for each product item based on its transaction history. By incorporating such best-selling prediction with relevance, we propose a new ranking model for product search, in which we rank higher the product items that are not only relevant to the customer's need but with higher probability to be purchased by the customer. Results of a large scale evaluation, conducted over the dataset from a commercial product search engine, demonstrate that our new ranking method is more effective for locating those product items that customers really desire to buy at higher rank positions without hurting the search relevance.