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This paper addresses the problem of suboptimal positive sample construction in implicit collaborative filtering by proposing Temporal Filtration-enhanced Positive Sample Set (TFPS). TFPS constructs a weighted user-item bipartite graph based on time decay derived from interaction time intervals, then uses filtering operations to create layered subgraphs, and finally employs a layer-enhancement strategy to generate a high-quality positive sample set. The authors theoretically justify the improvements in Recall@k and NDCG@k and empirically validate TFPS's effectiveness across three datasets, demonstrating its compatibility with existing implicit CF recommenders and negative sampling methods.
By explicitly modeling the temporal decay of user-item interactions, TFPS constructs more reliable positive samples, leading to significant improvements in recommendation accuracy compared to methods that focus solely on negative sampling.
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.
