Infrequent Purchased Product Recommendation Making Based on User Behaviour and Opinions in E-commerce Sites

Web based commercial recommender systems (RS) can help users to make decisions about which product to purchase from the vast amount of products available on the Internet. Currently, many commercial recommender systems are developed for recommending frequently purchased products where a large amount of explicit ratings or purchase history data is available to predict user preferences. However, for products that are infrequently purchased by users, it is difficult to collect such data and, thus, user profiling becomes a major challenge for recommending these kinds of products. This paper proposes a recommendation approach for infrequently purchased products based on user opinions and navigation data. User opinion data, which is collected from product review data, is used to generate product profiles and user navigation data is used to generate user profiles, both of which are used for recommending products that best satisfy the users´ needs. Experiments conducted on real e-commerce data show that the proposed approach, named, Adaptive Collaborative Filtering (ACF), which utilizes user and product profiles, outperforms the Query Expansion (QE) approach that only utilizes product profiles to recommend products. The ACF also performs better than the Basic Search (BS) approach, which is widely applied by the current e-commerce applications.