论文标题
重新思考基于会话的建议中的重要性
Rethinking Item Importance in Session-based Recommendation
论文作者
论文摘要
基于会话的建议旨在根据匿名会议来预测用户。先前的工作主要集中于正在进行的会话中项目之间的过渡关系。他们通常没有足够关注这些项目与用户主要意图的重要性的重要性。在本文中，我们提出了一种基于会话的推荐方法，其重要性提取模块，即SR-IEM，该方法在正在进行的会话中考虑了用户的长期和最近的行为。我们采用修改后的自我注意机制来估计会话中项目的重要性，然后将其用于预测用户的长期偏好。通过将用户的长期偏好和当前兴趣结合到最后一个互动项目所传达的当前利益来产生项目建议。在两个基准数据集上进行的实验验证了SR-IEM在召回和MRR方面优于启动，并且计算复杂性降低。
Session-based recommendation aims to predict users' based on anonymous sessions. Previous work mainly focuses on the transition relationship between items during an ongoing session. They generally fail to pay enough attention to the importance of the items in terms of their relevance to user's main intent. In this paper, we propose a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM, that considers both a user's long-term and recent behavior in an ongoing session. We employ a modified self-attention mechanism to estimate item importance in a session, which is then used to predict user's long-term preference. Item recommendations are produced by combining the user's long-term preference and current interest as conveyed by the last interacted item. Experiments conducted on two benchmark datasets validate that SR-IEM outperforms the start-of-the-art in terms of Recall and MRR and has a reduced computational complexity.
