论文标题
除了从下一个项目中学习：通过个性化利息可持续性进行顺序推荐
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability
论文作者
论文摘要
顺序推荐系统通过捕获用户的兴趣漂移显示了有效的建议。有两组现有的顺序模型：以用户和项目为中心的模型。以用户为中心的模型根据每个用户的顺序消费历史记录捕获个性化的利息漂移，但没有明确考虑用户对项目的兴趣是否超过培训时间，即利息可持续性。另一方面，以项目为中心的模型考虑了用户在培训时间后是否维持一般利益，但不是个性化的。在这项工作中，我们提出了一个推荐系统，在这两个类别中都具有模型的优势。我们提出的模型捕获了个性化的利息可持续性，表明每个用户对物品的兴趣是否会超出培训时间。我们首先制定一项任务，该任务需要根据用户的消费历史记录来预测每个用户在培训时间的最新阶段将消耗哪些项目。然后，我们提出简单而有效的方案，以增强用户的稀疏消费历史记录。广泛的实验表明，所提出的模型在11个现实世界数据集上的表现优于10个基线模型。这些代码可在https://github.com/dmhyun/peris上找到。
Sequential recommender systems have shown effective suggestions by capturing users' interest drift. There have been two groups of existing sequential models: user- and item-centric models. The user-centric models capture personalized interest drift based on each user's sequential consumption history, but do not explicitly consider whether users' interest in items sustains beyond the training time, i.e., interest sustainability. On the other hand, the item-centric models consider whether users' general interest sustains after the training time, but it is not personalized. In this work, we propose a recommender system taking advantages of the models in both categories. Our proposed model captures personalized interest sustainability, indicating whether each user's interest in items will sustain beyond the training time or not. We first formulate a task that requires to predict which items each user will consume in the recent period of the training time based on users' consumption history. We then propose simple yet effective schemes to augment users' sparse consumption history. Extensive experiments show that the proposed model outperforms 10 baseline models on 11 real-world datasets. The codes are available at https://github.com/dmhyun/PERIS.
