Multi-criteria matrix localization and integration in collaborative filtering-based recommendation

Abstract

There are usually more than one criterion considered when users choose an item. Although there have been studies of multi-criteria recommendations, existing approaches require multi-criteria ratings that are explicitly given by users. It is usually a burden for a user to provide more than one instance of feedback on an item

therefore, user feedback datasets are usually sparse when users are asked to provide multi-criteria ratings. Due to the sparsity of multi-criteria rating data, the similarity measurements used by the existing approaches may produce biased results, possi-bly leading to degradation of the recommendation accuracy. This problem becomes worse as the sparsity of a dataset increases. To overcome this problem and take advantage of using multi-criteria ratings, we proposed a multi-criteria matrix localization and integration (MCMLI) ap-proach for collaborative filtering in this paper. The main goal of MCMLI is to alleviate the ef-fects of the data-sparsity problem by generating and integrating cohesive user-item sub-groups (CUISs) for each criterion. The proposed approach is composed of three phases. At the first phase, a given user-item matrix is divided into a set of CUIS matrices, each of which is orga-nized with correlated users and items for each criterion. MCMLI repeats this CUIS generation process until the generated sub-groups cover all elements of the given user-item matrix. To gen-erate prediction results for each criterion, MCMLI then predicts user ratings on new items for each CUIS and aggregates the prediction results to make recommendations to users. To enable personalized recommendations, during the aggregation process, each user’s preferences on mul-tiple criteria are weighted differently according to the number of CUISs to which the user be-longs. We demonstrate the effectiveness of our approach by conducting an experiment with real-world datasets from TripAdvisor and Yahoo! Movies. The experimental results show that MCMLI outperforms existing multi-criteria collaborative-filtering-based recommendation meth-ods in terms of the recommendation accuracy. In addition, unlike the existing multi-criteria rec-ommendation approaches, even when the sparsity level of a dataset increases, the recommenda-tion accuracy of MCMLI does not de-crease significantly.