A Product-Customer Matching Framework for Web 2.0 Applications

Finding matching customers for a product is critical in many applications, especially in the e-commerce field. In this paper, we propose a novel product-customer matching framework to handle this issue, which consists of two components: data preprocessing and query processing. During the data preprocessing phase, a generation rule is proposed to learn the user's preference. With the spread of the web 2.0 applications, users like to rate some products they have experienced in the social applications, e.g. Dianping and Yelp. Hence, it is possible to construct users' preferences based on their rating information. In the query processing phase, we first propose Reverse Top-k-Ranks Query, which integrates reverse top-k query and reverse k-ranks query, to find some users to match the query product, and then devise an efficient method (BBPA) to handle this new query. Finally, we evaluate the efficiency and effectiveness of our matching framework upon real and synthetic datasets, showing that our framework works well in finding matching users for a query product.