BSPR: Basket-sensitive personalized ranking for product recommendation

Product recommendation has played an important role in improving user experiences and obtaining more profits. To optimize recommendation models, pairwise learning has become a mainstream method for modeling user preferences from implicit feedback. Nevertheless, most existing pairwise methods optimize the relative order among products from only the user perspective. In fact, the purchase decision making of a given user depends on not only individual taste, but also the complementary relationships between the recommended products and the products in his/her historical basket. We argue that it is challenging to uncover meaningful user and product representations by only utilizing the user-side pairwise ranking. Towards this end, we propose a novel probabilistic pairwise method named BSPR, short for basket-sensitive personalized ranking, which solves both user- and product-side pairwise ranking problems in a unified manner. Specifically, BSPR discovers mutual correlations among users and products by exploiting co-pairwise ranking, alleviating the inherent flaw in existing pairwise methods. Considering that the negative sampler is one of the key components for pairwise learning, we devise a position-aware sampling strategy for the proposed method. To solve the optimization problem in BSPR, we further design an alternative optimization algorithm to efficiently learn the model parameters. Extensive experiments on multiple real-world datasets demonstrate significant improvements of our method over a series of state-of-the-art methods. Our implementation of BSPR is publicly available at:https://github.com/wubinzzu/BSPR. (C) 2020 Elsevier Inc. All rights reserved.