TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering
This work provides an incremental improvement for researchers and practitioners working on collaborative filtering recommendation systems by enhancing positive sample construction, which can be integrated with existing recommenders.
This paper addresses the problem of constructing high-quality positive sample sets for implicit collaborative filtering by proposing TFPS, a temporal filtration-enhanced method. TFPS uses a time decay model to weight user-item interactions, layers the resulting graph, and applies a layer-enhancement strategy to build positive sample sets. Experiments on three real-world datasets demonstrate its effectiveness in improving Recall@k and NDCG@k.
The negative sampling strategy can effectively train collaborative filtering (CF) recommendation models based on implicit feedback by constructing positive and negative samples. However, existing methods primarily optimize the negative sampling process while neglecting the exploration of positive samples. Some denoising recommendation methods can be applied to denoise positive samples within negative sampling strategies, but they ignore temporal information. Existing work integrates sequential information during model aggregation but neglects time interval information, hindering accurate capture of users' current preferences. To address this problem, from a data perspective, we propose a novel temporal filtration-enhanced approach to construct a high-quality positive sample set. First, we design a time decay model based on interaction time intervals, transforming the original graph into a weighted user-item bipartite graph. Then, based on predefined filtering operations, the weighted user-item bipartite graph is layered. Finally, we design a layer-enhancement strategy to construct a high-quality positive sample set for the layered subgraphs. We provide theoretical insights into why TFPS can improve Recall@k and NDCG@k, and extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed method. Additionally, TFPS can be integrated with various implicit CF recommenders or negative sampling methods to enhance its performance.