ClustPTF: Clustering-based parallel tensor factorization for the diverse multi-criteria recommendation

초록

In the recommender system field, diversity as the measure of recommendation quality has gained much attention recently. However, many pieces of research have shown that it has a trade-off relation with predictive performance. To improve recommendation diversity and predictive performance in multi-criteria recommender systems, we propose a clustering-based parallel tensor factorization (ClustPTF). In the ClustPTF, sentiment analysis alleviates model sparsity, and the K-means clustering considering rating behaviors groups similar user preferences into sub-models and leads to improve recommendation diversity. The sub-models are then factorized in parallel to predict ratings in near real-time. With one dataset gathered from TripAdvisor, experiments showed that the ClustPTF considerably improve recommendation diversity (13.44x of a conventional tensor factorization (TF0)) and response time (23.13x of the TF0). Even its predictive performance is superior to the TF0 (41.06% improvement in MAE). Furthermore, the ClustPTF outperformed recent techniques in recommendation diversity and predictive performance (i.e., MAE and precision). © 2021 Elsevier B.V.

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