User identification network with contrastive clustering for shared-account recommendation

Abstract

The Shared-Account Recommendation (SAR) aims to accurately identify and accommodate the varied preferences of multiple users sharing a single account by analyzing their aggregated interactions. SAR faces challenges in preference identification when multiple users share an account. Existing Shared-Account Modeling (SAM) methods assume overly simplistic conditions and overlook the robustness of representations, leading to inaccurate embeddings that are susceptible to disturbances. To address limitations in existing SAR methods, we introduce the Contrastive Clustering User Identification Network (CCUI-Net) framework to enhance SAR. This framework employs graph-based transformations and node representation learning to refine user embeddings, utilizes hierarchical contrastive clustering for improved user identification and robustness against data noise, and leverages an attention mechanism to dynamically balance contributions from various users. These innovations significantly boost the precision and reliability of recommendations. Experimental results across four domains from the HVIDEO and HAMAZON datasets (E-domain and V-domain in HVIDEO, M-domain and B-domain in HAMAZON) demonstrate that CCUI-Net exceeds the performance of many existing available methods on the metrics MRR@5, MRR@20, Recall@5, and Recall@20. Specifically, the improvements in the M-domain and B-domain for Recall@5 and Recall@20 are 14.64%, 8.55%, 18.67%, and 9.59% respectively.