A new collaborative approach to solve the gray-sheep users problem in recommender systems

Abstract : Recommender systems aim to help users to find items that fit their requirements and preferences. In that field, the collaborative filtering (CF) approach is considered as a widely used one. There are two main approaches for CF: memory-based and model-based. Both of the two approaches are based on the use of users’ ratings to predict the top-N recommendation for the active user. Despite its simplicity and efficiency, The CF approach stills suffer from many drawbacks including sparsity, gray sheep and scalability. The aim of this work is to deal with the gray sheep problem, by proposing a novel collaborative filtering approach. This novel approach aims to enhance the accuracy of prediction by turning the users whose preferences disagree with the target user, into new similar neighbors. For instance, if a user X is dissimilar to a user Y then the user ¬ X is similar to the user Y. To evaluate the performance of the proposed approach, we have used two datasets including MovieLens and FilmTrust. The Experimental results show that our approach outperforms many traditional recommendation techniques.

Keywords : Recommender system; gray-sheep problem; Collaborative filtering; Opposite neighbors; Similarity Measure.

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Recommended citation: A. E. Fazziki, O. E. Aissaoui, Y. E. M. E. Alami, Y. E. Allioui and M. Benbrahim, “A new collaborative approach to solve the gray-sheep users problem in recommender systems,” 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS), Marrakech, Morocco, 2019, pp. 1-4, doi: 10.1109/ICDS47004.2019.8942256.