Recommender systems have achieved great success in modeling user's preferences on items and predicting the next item the user would consume. Recently, there have been many efforts to utilize time information of users' interactions with items to capture inherent temporal patterns of user behaviors and offer timely recommendations at a given time. Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items. However, we argue they are insufficient for fully learning the time information because the temporal patterns of user preference are usually heterogeneous. A user's preference for a particular item may 1) increase periodically or 2) evolve over time under the influence of significant recent events, and each of these two kinds of temporal pattern appears with some unique characteristics. In this paper, we first define the unique characteristics of the two kinds of temporal pattern of user preference that should be considered in time-aware recommender systems. Then we propose a novel recommender system for timely recommendations, called TimelyRec, which jointly learns the heterogeneous temporal patterns of user preference considering all of the defined characteristics. In TimelyRec, a cascade of two encoders captures the temporal patterns of user preference using a proposed attention module for each encoder. Moreover, we introduce an evaluation scenario that evaluates the performance on predicting an interesting item and when to recommend the item simultaneously in top-K recommendation (i.e., item-timing recommendation). Our extensive experiments on a scenario for item recommendation and the proposed scenario for item-timing recommendation on real-world datasets demonstrate the superiority of TimelyRec and the proposed attention modules.

中文翻译：

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学习用户偏好的异构时间模式以进行及时推荐

推荐系统在建模用户对商品的偏好以及预测用户将消费的下一个商品方面已经取得了巨大的成功。近来，已经进行了许多努力来利用用户与项目交互的时间信息来捕获用户行为的固有时间模式并在给定时间提供及时的建议。现有研究将时间信息视为一种单独的功能，并着重于如何将其与用户对商品的偏好相关联。但是，我们认为它们不足以完全学习时间信息，因为用户偏好的时间模式通常是异构的。用户对特定商品的偏好可能会：1）定期增加或2）在近期发生的重大事件的影响下随着时间的推移而发展，并且这两种时间模式都呈现出一些独特的特征。在本文中，我们首先定义了在时间感知推荐系统中应考虑的两种用户偏好时间模式的独特特征。然后，我们提出了一种用于及时推荐的新型推荐系统，称为TimelyRec，该系统结合所有定义的特征共同学习用户偏好的异构时间模式。在TimelyRec中，两个编码器的级联使用针对每个编码器的建议关注模块捕获用户偏好的时间模式。此外，我们介绍了一种评估方案，该方案评估了在前K个推荐（即项目定时推荐）中预测有趣项目以及何时同时推荐该项目的性能。