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.

产品推荐在改善用户体验和获得更多利润方面发挥了重要作用。为了优化推荐模型，成对学习已成为从隐式反馈建模用户偏好的主流方法。但是，大多数现有的成对方法仅从用户角度优化产品之间的相对顺序。实际上，给定用户的购买决策不仅取决于个人口味，还取决于推荐产品与其历史篮子中产品之间的互补关系。我们认为，仅利用用户侧成对排名来揭示有意义的用户和产品表示形式是一项挑战。为此，我们提出了一种新的概率成对方法，称为BSPR，简称BSPR。购物篮敏感的个性化排名，以统一的方式解决了用户端和产品端的成对排名问题。具体地说，BSPR通过利用成对排名来发现用户和产品之间的相互关系，从而减轻了现有成对方法中的固有缺陷。考虑到负采样器是成对学习的关键组件之一，我们针对该方法设计了一种位置感知的采样策略。为了解决BSPR中的优化问题，我们进一步设计了另一种优化算法来有效地学习模型参数。在多个真实世界的数据集上进行的广泛实验表明，我们的方法相对于一系列最新方法有了显着改进。我们的BSPR实施可从以下网址公开获得：https：//github.com/wubinzzu/BSPR。