Recommendation based on multiproduct utility maximization

Recommender systems often recommend several products to a user at the same time, but with little consideration of the relationships among the recommended products. We argue that relationships such as substitutes and complements are crucial, since the utility of one product may depend on whether or not other products are purchased. For example, the utility of a camera lens is much higher if the user has the appropriate camera (complements), and the utility of one camera is lower if the user already has a similar camera (substitutes). In this paper, we propose multi-product utility maximization (MPUM) as a general approach to account for product relationships in recommendation systems. MPUM integrates the economic theory of consumer choice theory with personalized recommendation, and explicitly considers product relationships. It describes and predicts utility of product bundles for individual users. Based on MPUM, the system can recommend products by considering what the users already have, or recommend multiple products with maximum joint utility. As the estimated utility has mon- etary unit, other economic based evaluation metrics such as consumer surplus or total surplus can be incorporated naturally. We evaluate MPUM against several popular base- line recommendation algorithms on two offline E-commerce datasets. The experimental results showed that MPUM significantly outperformed baseline algorithm under top-K evaluation metric, which suggests that the expected number of accepted/purchased products given K recommendations are higher.