Decrease and conquer-based parallel tensor factorization for diversity and real-time of multi-criteria recommendation

Abstract

In the field of recommender systems, diversity as the measure of recommendation quality has gained much attention recently. Unfortunately, many researchers have shown that it has a trade-off relation with accuracy. Meanwhile, tensor factorization has been used as a useful technique that considers multi-correlations between user-item-other factors directly. However, it generally suffers from the model sparsity caused by high dimensionality and requirement of high computational costs. To improve diversity and response time while preserving accuracy in multi-criteria recommender systems (MCRS), we propose a decrease and conquer-based parallel tensor factorization (DnCPTF). In the DnCPTF, sentiment analysis alleviates the sparsity problem, and a two-phase clustering groups similar user reviews into sub-models. Furthermore, a controllable subdivision guarantees high diversity and short response time. The sub-models are then factorized in parallel to predict ratings, and top-N items are recommended via ratings consolidated from the sub-models. On a real-world dataset gathered from TripAdvisor, experimental results demonstrated that the DnCPTF significantly improve recommendation diversity (55× of a conventional tensor factorization (CTF)) and response time (182× of the CTF) with preserving high precision and recall. Furthermore, it outperformed recent techniques in precision, diversity and required response time within 1 s on average.