TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering

TL;DR

This paper introduces TFPS, a novel method that leverages temporal information to construct high-quality positive sample sets, significantly improving the performance of implicit collaborative filtering models.

Contribution

The paper proposes a temporal filtration-based approach to enhance positive sample set construction, integrating time decay and layered filtering to improve recommendation accuracy.

Findings

TFPS improves Recall@k and NDCG@k metrics.

Extensive experiments validate TFPS's effectiveness on real-world datasets.

TFPS can be integrated with various CF recommenders and negative sampling methods.

Abstract

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.