Exploiting temporal effect has empirically been recognized as a promising way to improve recommendation performance in recent years. In real-world applications, one-class data in the form of (user, item, timestamp) are usually more accessible and abundant than numerical ratings. In this article, we focus on exploiting such one-class data in order to provide personalized next-item recommendation services. Specifically, we base our work on the framework of time-aware item-based collaborative filtering and propose a simple yet effective similarity measurement called bidirectional item similarity (BIS) that is able to capture sequential patterns even from noisy data. Furthermore, we extend BIS via some factorization techniques and obtain an adaptive version, i.e., adaptive BIS (ABIS), in order to better fit the behavioral data. We also design a compound weighting function that leverages the complementarity between two well-known time-aware weighting functions. With the proposed similarity measurements and weighting function, we obtain two novel collaborative filtering methods that are able to achieve significantly better performance than the state-of-the-art methods, showcasing their effectiveness for next-item recommendation.

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通过具有双向项目相似性的协同过滤的下一个项目推荐

近年来，利用时间效应在经验上被认为是提高推荐性能的一种有前途的方法。在实际应用中，（用户、项目、时间戳）形式的一类数据通常比数字评级更容易访问和丰富。在本文中，我们专注于利用此类数据来提供个性化的下一项推荐服务。具体来说，我们的工作基于时间感知的基于项目的协同过滤框架，并提出了一种简单而有效的相似度测量方法，称为双向项目相似度 (BIS)，它甚至能够从噪声数据中捕获顺序模式。此外，我们通过一些分解技术扩展了BIS，并获得了一个自适应版本，即自适应BIS（ABIS），以更好地拟合行为数据。我们还设计了一个复合加权函数，它利用了两个众所周知的时间感知加权函数之间的互补性。通过提出的相似度测量和加权函数，我们获得了两种新的协同过滤方法，它们能够实现比最先进的方法更好的性能，展示了它们对下一个项目推荐的有效性。