论文标题
跨域顺序建议的混合信息流
Mixed Information Flow for Cross-domain Sequential Recommendations
论文作者
论文摘要
跨域顺序建议是预测用户最有可能基于来自多个域的顺序行为相互作用的下一个项目的任务。跨域顺序推荐的主要挑战之一是从多个域中掌握和传递信息流,以促进所有域中的建议。先前的研究通过探索来自不同领域的项目之间的联系来研究行为信息的流动。到目前为止,知识流(即,来自不同领域的知识之间的联系)已被忽略。在本文中,我们提出了一个混合信息流网络,用于跨域顺序建议,以通过合并行为转移单元和知识传递单元来考虑行为信息的流和知识流。提出的混合信息流网络能够决定何时应该使用跨域信息,如果是,则应使用哪种跨域信息根据用户当前的偏好来丰富序列表示。在四个电子商务数据集上进行的广泛实验表明,混合信息流网络能够通过建模混合信息流来进一步提高不同域中的建议性能。
Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. Previous studies have investigated the flow of behavioral information by exploring the connection between items from different domains. The flow of knowledge (i.e., the connection between knowledge from different domains) has so far been neglected. In this paper, we propose a mixed information flow network for cross-domain sequential recommendation to consider both the flow of behavioral information and the flow of knowledge by incorporating a behavior transfer unit and a knowledge transfer unit. The proposed mixed information flow network is able to decide when cross-domain information should be used and, if so, which cross-domain information should be used to enrich the sequence representation according to users' current preferences. Extensive experiments conducted on four e-commerce datasets demonstrate that mixed information flow network is able to further improve recommendation performance in different domains by modeling mixed information flow.