论文标题
PA：顺序推荐的位置感知相似性测量
PAS: A Position-Aware Similarity Measurement for Sequential Recommendation
论文作者
论文摘要
在配备某些项目到项目相似性测量时，基于项目的共同协作过滤框架成为一种典型的推荐方法。一方面，我们意识到，精心设计的相似性测量是提供令人满意的建议服务的关键。另一方面，推荐系统社区很少研究为顺序推荐设计的相似性测量。因此，在本文中，我们专注于设计一种新颖的相似性测量，称为位置感知相似性（PAS），以进行顺序建议。据我们所知，所提出的PA是第一个基于计数的相似性测量，该测量同时捕获了从历史用户行为数据和输入序列中的项目位置信息中捕获顺序模式。我们在四个公共数据集上进行了广泛的经验研究，在该数据集中，我们提出的基于PAS的方法甚至与最新的顺序推荐方法相比表现出竞争性能，包括一种非常最新的基于相似性的方法和两种基于GNN的方法。
The common item-based collaborative filtering framework becomes a typical recommendation method when equipped with a certain item-to-item similarity measurement. On one hand, we realize that a well-designed similarity measurement is the key to providing satisfactory recommendation services. On the other hand, similarity measurements designed for sequential recommendation are rarely studied by the recommender systems community. Hence in this paper, we focus on devising a novel similarity measurement called position-aware similarity (PAS) for sequential recommendation. The proposed PAS is, to our knowledge, the first count-based similarity measurement that concurrently captures the sequential patterns from the historical user behavior data and from the item position information within the input sequences. We conduct extensive empirical studies on four public datasets, in which our proposed PAS-based method exhibits competitive performance even compared to the state-of-the-art sequential recommendation methods, including a very recent similarity-based method and two GNN-based methods.
