约束条件下买方对卖方的推荐
Buyer to Seller Recommendation under Constraints
摘要：
大多数推荐系统被设计为向用户推荐项目（例如电影和产品）。我们专注于向卖家推荐买家的问题，这带来了新的挑战：（1）对买家在他们变得不知所措之前所参与的推荐数量的约束，（2）对卖家在其预算内接收的推荐数量的约束，以及（3）对卖家想要接收的买家集合的约束（例如，同一个家庭不超过两个人）。我们提出了以下关键问题的推荐买家卖家：约束推荐（C-REC）捕获前两个挑战，并在同一时间捕获所有三个挑战的约束推荐（CAC-REC）。我们表明，C-REC可以使用线性规划建模，并可以有效地解决使用现代求解器。另一方面，我们证明了CAC-REC是NP-难的。我们提出了两种近似算法来解决CAC-REC，并通过使用真实世界数据集的综合实验表明，它们可以接近最优解。
英文摘要：
The majority of recommender systems are designed to recommend items (such as movies and products) to users. We focus on the problem of recommending buyers to sellers which comes with new challenges: (1) constraints on the number of recommendations buyers are part of before they become overwhelmed, (2) constraints on the number of recommendations sellers receive within their budget, and (3) constraints on the set of buyers that sellers want to receive (e.g., no more than two people from the same household). We propose the following critical problems of recommending buyers to sellers: Constrained Recommendation (C-REC) capturing the first two challenges, and Conflict-Aware Constrained Recommendation (CAC-REC) capturing all three challenges at the same time. We show that C-REC can be modeled using linear programming and can be efficiently solved using modern solvers. On the other hand, we show that CAC-REC is NP-hard. We propose two approximate algorithms to solve CAC-REC and show that they achieve close to optimal solutions via comprehensive experiments using real-world datasets.
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