A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques

The importance of recommendation systems for business applications has led to extensive research efforts to improve the recommendations accuracy as well as to reduce the sparsity problem. Despite the success of both collaborative filtering and multi-criteria approaches, they still need to be further optimized to address the stated problems. In this paper, we propose a new hybrid method based on enhanced fuzzy multi-criteria collaborative filtering which incorporates demographic information and an item-based ontological semantic filtering approach for movie recommendation purposes. We use an adaptive neuro-fuzzy inference system to discover the relationship between each criterion and the overall rating. A fusion of fuzzy cosine and Jaccard similarities is further adopted to calculate the total similarity between users/movies with respect to the effect of co-rated item set cardinality on the reliability of similarity measures. To increase the robustness and reliability of the final similarity measure, especially in the case of cold start users, a convex combination of both user and movie based similarities is used; in which the convex weightings are determined through the gradient decent algorithm to ensure a minimum prediction error. Experimental results demonstrate the efficiency of the proposed method in reducing the sparsity problem and improving the prediction accuracy. (C) 2017 Elsevier B.V. All rights reserved.