Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
IEEE Transactions on Knowledge and Data Engineering
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Show me the money!: deriving the pricing power of product features by mining consumer reviews
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-objective query processing for database systems
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Ranking refinement and its application to information retrieval
Proceedings of the 17th international conference on World Wide Web
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Webpage understanding: beyond page-level search
ACM SIGMOD Record
IEEE Transactions on Knowledge and Data Engineering
A demo search engine for products
Proceedings of the 20th international conference companion on World wide web
Increasing temporal diversity with purchase intervals
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Discovering informative social subgraphs and predicting pairwise relationships from group photos
Proceedings of the 20th ACM international conference on Multimedia
People search and activity mining in large-scale community-contributed photos
Proceedings of the 20th ACM international conference on Multimedia
Enhancing product search by best-selling prediction in e-commerce
Proceedings of the 21st ACM international conference on Information and knowledge management
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With the growing pervasiveness of the Internet, online search for products and services is constantly increasing. Most product search engines are based on adaptations of theoretical models devised for information retrieval. However, the decision mechanism that underlies the process of buying a product is different than the process of locating relevant documents or objects. We propose a theory model for product search based on expected utility theory from economics. Specifically, we propose a ranking technique in which we rank highest the products that generate the highest surplus, after the purchase. In a sense, the top ranked products are the "best value for money" for a specific user. Our approach builds on research on "demand estimation" from economics and presents a solid theoretical foundation on which further research can build on. We build algorithms that take into account consumer demographics, heterogeneity of consumer preferences, and also account for the varying price of the products. We show how to achieve this without knowing the demographics or purchasing histories of individual consumers but by using aggregate demand data. We evaluate our work, by applying the techniques on hotel search. Our extensive user studies, using more than 15,000 user-provided ranking comparisons, demonstrate an overwhelming preference for the rankings generated by our techniques, compared to a large number of existing strong state-of-the-art baselines.