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Recommendation for Repeat Consumption from User Implicit Feedback

机译：用户隐式反馈的重复消费建议

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摘要

Recommender system has been studied as a useful tool to discover novel items for users while fitting their personalized interest. Thus, the previously consumed items are usually out of consideration due to the “lack” of novelty. However, as time elapses, people may forget those previously consumed and preferred items which could become “novel” again. Meanwhile, repeat consumption accounts for a major portion of people's observed activities; examples include: eating regularly at a same restaurant, or repeatedly listening to the same songs. Therefore, we believe that recommending repeat consumption will have a real utility at certain times. In this paper, we formulate the problem of recommendation for repeat consumption with user implicit feedback. A time-sensitive personalized pairwise ranking (TS-PPR) method based on user behavioral features is proposed to address this problem. The proposed method factorizes the temporal user-item interactions via learning the mappings from the behavioral features in observable space to the preference features in latent space, and combines users' static and dynamic preferences together in recommendation. An empirical study on real-world data sets shows encouraging results.
机译：推荐系统已经被研究为在满足用户个性化兴趣的同时为用户发现新颖物品的有用工具。因此，由于缺乏“新颖性”，通常不会考虑先前消耗的物品。但是，随着时间的流逝，人们可能会忘记那些以前消费过的，偏爱的物品，这些物品可能会再次变得“新颖”。同时，重复消费占人们观察到的活动的很大一部分；例如：在同一家餐厅定期进餐，或反复听同一首歌。因此，我们认为建议重复消费在某些时候会有实际作用。在本文中，我们制定了具有用户隐式反馈的重复消费推荐问题。提出了一种基于用户行为特征的时敏个性化成对排名（TS-PPR）方法。所提出的方法通过学习从可观察空间中的行为特征到潜在空间中的偏好特征的映射来分解时间上的用户-项目交互，并在推荐中将用户的静态和动态偏好组合在一起。对现实世界数据集的实证研究显示令人鼓舞的结果。

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