Personalized recommendation via cross-domain triadic factorization

TLDR

Collaborative filtering is a key recommender technique, but data sparsity and missing feedback in uninvolved domains limit cross‑domain collaborative filtering performance. This work introduces a generalized Cross‑Domain Triadic Factorization model that captures interactions among domain‑specific user and item factors. Two CDTF algorithms are developed—one for explicit and one for implicit feedback—augmented by a genetic‑algorithm weight‑tuning scheme, and their effectiveness is evaluated on two real‑world datasets against state‑of‑the‑art baselines. Experimental results demonstrate that the proposed models outperform competing approaches.

Abstract

Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic in recent years. However, due to the lack of sufficient dense explicit feedbacks and even no feedback available in users' uninvolved domains, current CDCF approaches may not perform satisfactorily in user preference prediction. In this paper, we propose a generalized Cross Domain Triadic Factorization (CDTF) model over the triadic relation user-item-domain, which can better capture the interactions between domain-specific user factors and item factors. In particular, we devise two CDTF algorithms to leverage user explicit and implicit feedbacks respectively, along with a genetic algorithm based weight parameters tuning algorithm to trade off influence among domains optimally. Finally, we conduct experiments to evaluate our models and compare with other state-of-the-art models by using two real world datasets. The results show the superiority of our models against other comparative models.