Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system

Collaborative Filtering (CF) is the most widely used prediction technique in recommender systems. It makes recommendations based on ratings that users have assigned to items. Most of the current CF recommender systems maintain only single user ratings inside the user-item ratings matrix. Multi-criteria based CF presents a possibility of providing accurate recommendations by considering the user preferences in multi aspects of items. However, in the multi-criteria CF, the user behavior about items’ features is frequently subjective, imprecise and vague. These in turn induce uncertainty in reasoning and representation of items’ features that exactly cannot be solved using crisp machine learning techniques. In contrast, approaches such as fuzzy methods instead of crisp methods can better solve the issue of uncertainty. In addition, fuzzy methods can predict the users’ preference more accurately and even better alleviate the sparsity problem in overall rating by considering user perception about items’ features. Apart from this, in the multi-criteria CF, users provide the ratings on different aspects (criteria) of an item in new dimensions; thereby, increasing the scalability problem. Appropriate dimensionality reduction techniques are thus needed to capture the high dimensions all together without reducing them into lower dimensions to reveal the latent associations among the components. This study presents a new model for multi-criteria CF using Adaptive Neuro-Fuzzy Inference System (ANFIS) combined with subtractive clustering and Higher Order Singular Value Decomposition (HOSVD). HOSVD is used for dimensionality reduction for improving the scalability problem and ANFIS is used for extracting fuzzy rules from the experimental dataset, alleviating the sparsity problems in overall ratings and representing and reasoning the users’ behavior on items’ features. Experimental results on real-world dataset show that combination of two techniques remarkably improves the predictive accuracy and recommendation quality of multi-criteria CF.