TDIC: Time-Aware Disentanglement of Interest and Conformity in Mobile App Recommendations

Qibo Li, Yuqi Zhao, Yutao Ma

Abstract: Popularity bias in mobile APP recommendations skews results by over-prioritizing trending APPs, obscuring niche yet relevant alternatives, and conflating user interest with social conformity. To address this problem, we propose TDIC (short for Time-aware Disentanglement of Interest and Conformity), a novel causal framework tailored for personalized, debiased mobile APP suggestions. TDIC employs causal graph analysis to isolate user interest from conformity within interaction patterns, incorporates item quality to refine popularity judgments, and integrates temporal awareness to track the fluid nature of trends. TDIC disentangles authentic user intents from dynamic social influences, fostering unbiased and precise recommendations. Evaluated on two real-world datasets, MobileRec and Myket, TDIC demonstrates superior performance over state-of-the-art baselines, achieving gains of up to 17.24% in Recall@20 and 7.14% in NDCG@20 on MobileRec, and 2.55% in NDCG@50 on Myket. These outcomes highlight the crucial role of temporal dynamics and quality adjustments in mitigating popularity bias for mobile APP recommendations. Our code is available at https://github.com/ssea-lab/TDIC.

External IDs:dblp:conf/icws/LiZM25