The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes. In everyday life, large amounts of user feedback data are created along with user-item online interactions in a variety of ways, such as browsing, purchasing, and sharing. These multiple types of user feedback provide us with tremendous opportunities to detect individuals' fine-grained preferences. Different from most existing recommender systems that rely on a single type of feedback, we advocate incorporating multiple types of user-item interactions for better recommendations. Based on the observation that the underlying spectrum of user preferences is reflected in various types of interactions with items and can be uncovered by latent relational learning in metric space, we propose a unified neural learning framework, named Multi-Relational Memory Network (MRMN). It can not only model fine-grained user-item relations but also enable us to discriminate between feedback types in terms of the strength and diversity of user preferences. Extensive experiments show that the proposed MRMN model outperforms competitive state-of-the-art algorithms in a wide range of scenarios, including e-commerce, local services, and job recommendations.
翻译:现代在线平台中推荐人系统的成功与准确捕捉用户个人口味是不可分割的。在日常生活中,大量用户反馈数据与用户项目在线互动一起以多种方式产生,如浏览、购买和共享。这些多种用户反馈为我们提供了发现个人细微偏好的巨大机会。不同于大多数依靠单一类型反馈的现有推荐人系统,我们主张将多种用户项目互动纳入其中,以便提出更好的建议。基于用户偏好的基本范围反映在与项目的各种互动中,并且可以通过在计量空间进行潜在的关联学习来发现,我们提出了一个统一的神经学习框架,名为多关系记忆网络(MRMN)。它不仅能够模拟微小的用户项目关系,而且还使我们能够在用户偏好的力度和多样性方面区分反馈类型。广泛的实验表明,拟议的MMNM模型在广泛的情景中,包括电子商务、当地服务和工作建议,超越了具有竞争力的状态算法。