基于会话推荐的意图引导协同机器

Zhiqiang Pan, Fei Cai, Yanxiang Ling, M. de Rijke
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引用次数: 24

摘要

基于会话的推荐主要基于匿名会话产生项目预测。以前的研究利用来自邻居会话的协作信息来提高给定正在进行的会话的推荐准确性。以前的工作通常选择最近的会话作为候选邻居,因此无法识别最相关的邻居以获得有效的邻居表示。此外,很少有现有的方法同时考虑正在进行的会话中的顺序信号和最近的兴趣。本文介绍了一种基于会话推荐(ICM-SR)的意向引导协同机器。ICM-SR通过利用先前的顺序项和最后一项来编码正在进行的会话,以生成准确的会话表示,然后将其用于生成初始项预测作为意图。然后,我们设计了一个意图引导的邻居检测器来定位正确的邻居会话。最后,通过门控融合层自适应结合当前会话和邻居会话的表示,产生最终的项目推荐。在两个公共基准数据集上进行的实验表明,ICM-SR在召回率和MRR方面比最先进的基线有了显著的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intent-guided Collaborative Machine for Session-based Recommendation
Session-based recommendation produces item predictions mainly based on anonymous sessions. Previous studies have leveraged collaborative information from neighbor sessions to boost the recommendation accuracy for a given ongoing session. Previous work often selects the most recent sessions as candidate neighbors, thereby failing to identify the most related neighbors to obtain an effective neighbor representation. In addition, few existing methods simultaneously consider the sequential signal and the most recent interest in an ongoing session. In this paper, we introduce an Intent-guided Collaborative Machine for Session-based Recommendation (ICM-SR). ICM-SR encodes an ongoing session by leveraging the prior sequential items and the last item to generate an accurate session representation, which is then used to produce initial item predictions as intent. After that, we design an intent-guided neighbor detector to locate the correct neighbor sessions. Finally, the representations of the current session and the neighbor sessions are adaptively combined by a gated fusion layer to produce the final item recommendations. Experiments conducted on two public benchmark datasets show that ICM-SR achieves a significant improvement in terms of Recall and MRR over the state-of-the-art baselines.
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