Category-aware Collaborative Sequential Recommendation
Category-aware Collaborative Sequential Recommendation
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DOI：
10.1145/3404835.3462832
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Renqin Cai;Jibang Wu;Aidan San;Chong Wang;Hongning Wang
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文献类型：
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作者：
Renqin Cai;Jibang Wu;Aidan San;Chong Wang;Hongning Wang
顺序推荐是根据用户的交互历史预测用户下一个项目的任务。准确地建模下一个动作对过去动作的依赖性对于这个问题至关重要。此外，顺序推荐往往面临严重稀疏的项目到项目的用户的动作序列的过渡，这限制了这种解决方案的实际效用。为了应对这些挑战，我们提出了一个类别感知的协作顺序推荐。我们的初步统计测试表明，在类别中的项目到项目的过渡往往是更强的指标比一般项目到项目的过渡观察到的原始序列中的下一个项目。我们的方法以两种方式使用项目类别。首先，推荐器利用项目类别来组织用户自己的动作，以增强依赖模型的基础上，她自己的过去的行动。它利用自我注意力来捕获类别内转换模式，并根据最近动作的类别确定要考虑哪些类别内转换模式。其次，推荐器利用项目类别检索具有相似类别偏好的用户，以增强用户之间的协作学习，从而克服稀疏性。它利用注意力将从检索到的用户的目标用户的类别中的过渡模式。在两个大型数据集上的大量实验证明了我们的解决方案对大量最先进的顺序推荐模型的有效性。
Sequential recommendation is the task of predicting the next items for users based on their interaction history. Modeling the dependence of the next action on the past actions accurately is crucial to this problem. Moreover, sequential recommendation often faces serious sparsity of item-to-item transitions in a user's action sequence, which limits the practical utility of such solutions. To tackle these challenges, we propose a Category-aware Collaborative Sequential Recommender. Our preliminary statistical tests demonstrate that the in-category item-to-item transitions are often much stronger indicators of the next items than the general item-to-item transitions observed in the original sequence. Our method makes use of item category in two ways. First, the recommender utilizes item category to organize a user's own actions to enhance dependency modeling based on her own past actions. It utilizes self-attention to capture in-category transition patterns, and determines which of the in-category transition patterns to consider based on the categories of recent actions. Second, the recommender utilizes the item category to retrieve users with similar in-category preferences to enhance collaborative learning across users, and thus conquer sparsity. It utilizes attention to incorporate in-category transition patterns from the retrieved users for the target user. Extensive experiments on two large datasets prove the effectiveness of our solution against an extensive list of state-of-the-art sequential recommendation models.