Sequential recommendation systems aim to exploit users’ sequential behavior patterns to capture their interaction intentions and improve recommendation accuracy. Existing sequential recommendation methods mainly focus on modeling the items’ chronological relationships in each individual user behavior sequence, which may not be effective in making accurate and robust recommendations. On one hand, the performance of existing sequential recommendation methods is usually sensitive to the length of a user’s behavior sequence (i.e., the list of a user’s historically interacted items). On the other hand, besides the context information in each individual user behavior sequence, the collaborative information among different users’ behavior sequences is also crucial to make accurate recommendations. However, this kind of information is usually ignored by existing sequential recommendation methods. In this work, we propose a new sequential recommendation framework, which encodes the context information in each individual user behavior sequence as well as the collaborative information among the behavior sequences of different users, through building a local dependency graph for each item. We conduct extensive experiments to compare the proposed model with state-of-the-art sequential recommendation methods on five benchmark datasets. The experimental results demonstrate that the proposed model is able to achieve better recommendation performance than existing methods, by incorporating collaborative information.

序列推荐系统旨在利用用户的序列行为模式来捕获他们的交互意图并提高推荐准确性。现有的顺序推荐方法主要集中于对每个用户行为序列中的项目的时间关系进行建模，这可能无法有效地做出准确且鲁棒的推荐。一方面，现有顺序推荐方法的性能通常对用户行为序列(即，用户历史交互项目的列表)的长度敏感。另一方面，除了每个单独用户行为序列中的上下文信息之外，不同用户行为序列之间的协作信息对于做出准确的推荐也至关重要。然而，现有的顺序推荐方法通常会忽略此类信息。在这项工作中，我们提出了一种新的顺序推荐框架，通过为每个项目构建局部依赖图，对每个单独用户行为序列中的上下文信息以及不同用户行为序列之间的协作信息进行编码。我们进行了大量的实验，在五个基准数据集上将所提出的模型与最先进的顺序推荐方法进行比较。实验结果表明，通过结合协作信息，所提出的模型能够比现有方法实现更好的推荐性能。