MPIF in E-Commerce Recommendation: Application of Multi-Pairwise Ranking with Heterogeneous Implicit Feedback

To address the one-class collaborative filtering (OCCF) issue in e-commerce recommendation with only positive implicit feedback, mainstream methods adopt pairwise preference learning represented by Bayesian Personalized Ranking (BPR). However, BPR relies on an invalid assumption and suffers from severe data sparsity. This paper proposes Multi-pairwise Ranking with Heterogeneous Implicit Feedback (MPIF), which exploits heterogeneous implicit and auxiliary information to mine deep user preferences, constructs six pairwise preferences for classified items, and optimizes the model via stochastic gradient descent (SGD). Experiments on three real-world datasets verify that MPIF+ outperforms all state-of-the-art baselines on Normalized Discounted Cumulative Gain at rank 5 (NDCG@5), Precision at rank 5 (Pre@5), Recall at rank 5 (Rec@5), and Area Under Curve (AUC). It yields maximum improvements of 34.2%, 5.5%, and 32.9% on NDCG@5 for the Sobazaar, Retailrocket, and REES46 datasets, respectively, achieving significant and stable recommendation gains.