Multi-criteria matrix localization and integration for personalized collaborative filtering in IoT environments

Abstract

Collaborative filtering (CF)-based recommender systems can be used to deal with the complexity problem of users when they want to identify possible tasks on the fly and perform desired tasks by using various smart objects in Internet of Things (IoT) environments. However, in order to use CF-based recommender systems, users need to provide their feedbacks and 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, possibly leading to degradation of the recommendation accuracy. This problem becomes worse as the sparsity of a dataset increases. To alleviate the effects of the data-sparsity problem, and to take advantage of using multi-criteria ratings, we proposed a multi-criteria matrix localization and integration (MCMLI) approach for collaborative filtering in this paper. The main goal of MCMLI is to find cohesive user-item subgroups (CUISs) for each criterion from sparse data, and to predict users' interests for each criterion in a more precise manner. 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 organized with correlated users and items for each criterion. MCMLI repeats this CUIS generation process until the generated subgroups cover all elements of the given user-item matrix. To generate 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 multiple criteria are weighted differently according to the number of CUISs to which the user belongs. 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 methods in terms of the recommendation accuracy. In addition, unlike the existing multi-criteria recommendation approaches, even when the sparsity level of a dataset increases, the recommendation accuracy of MCMLI does not decrease significantly.