论文标题
通过逻辑推理网络提出高阶互补建议
Towards High-Order Complementary Recommendation via Logical Reasoning Network
论文作者
论文摘要
互补建议在电子商务中的关注越来越多，因为它加快了在购物旅程中为用户寻找经常购买产品的过程。因此，学习可以反映这种互补关系的产品表示形式在现代推荐系统中起着核心作用。在这项工作中，我们建议一个逻辑推理网络LogiRec，以有效地学习产品的嵌入以及它们之间的各种转换（投影，相交，否定）。 LogiRec能够捕获产品之间的不对称互补关系，并无缝地扩展到高级建议，在这些建议中学习了更全面和有意义的互补关系。最后，我们进一步提出了一个共同优化用于学习更通用的产品表示的混合网络。我们在低阶和高级建议方案下，根据各种基于排名的指标，我们在多个公共现实世界数据集上演示了我们的logirec在多个公共现实世界中的有效性。
Complementary recommendation gains increasing attention in e-commerce since it expedites the process of finding frequently-bought-with products for users in their shopping journey. Therefore, learning the product representation that can reflect this complementary relationship plays a central role in modern recommender systems. In this work, we propose a logical reasoning network, LOGIREC, to effectively learn embeddings of products as well as various transformations (projection, intersection, negation) between them. LOGIREC is capable of capturing the asymmetric complementary relationship between products and seamlessly extending to high-order recommendations where more comprehensive and meaningful complementary relationship is learned for a query set of products. Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation. We demonstrate the effectiveness of our LOGIREC on multiple public real-world datasets in terms of various ranking-based metrics under both low-order and high-order recommendation scenarios.
