A Topology-Aware Positive Sample Set Construction and Feature Optimization Method in Implicit Collaborative Filtering

TL;DR

This paper introduces TPSC-FO, a novel method that leverages topological community structures and neighborhood features to improve positive sample construction and feature optimization in implicit collaborative filtering, reducing false negatives and enhancing model accuracy.

Contribution

The paper proposes a topology-aware approach for positive sample set construction and feature optimization, addressing false negatives more effectively than existing methods.

Findings

Outperforms existing methods on five real-world datasets

Effectively identifies false negatives using topological community detection

Improves recommendation accuracy through neighborhood-guided feature refinement

Abstract

Negative sampling strategies are widely used in implicit collaborative filtering to address issues like data sparsity and class imbalance. However, these methods often introduce false negatives, hindering the model's ability to accurately learn users' latent preferences. To mitigate this problem, existing methods adjust the negative sampling distribution based on statistical features from model training or the hardness of negative samples. Nevertheless, these methods face two key limitations: (1) over-reliance on the model's current representation capabilities; (2) failure to leverage the potential of false negatives as latent positive samples to guide model learning of user preferences more accurately. To address the above issues, we propose a Topology-aware Positive Sample Set Construction and Feature Optimization method (TPSC-FO). First, we design a simple topological community-aware false negative identification (FNI) method and observe that topological community structures in interaction networks can effectively identify false negatives. Motivated by this, we develop a topology-aware positive sample set construction module. This module employs a differential community detection strategy to capture topological community structures in implicit feedback, coupled with personalized noise filtration to reliably identify false negatives and convert them into positive samples. Additionally, we introduce a neighborhood-guided feature optimization module that refines positive sample features by incorporating neighborhood features in the embedding space, effectively mitigating noise in the positive samples. Extensive experiments on five real-world datasets and two synthetic datasets validate the effectiveness of TPSC-FO.