Regresar

An efficient recommender system method based on the numerical relevances and the non-numerical structures of the ratings

Abstract:

In this paper, we propose a collaborative filtering method designed to improve the current memory-based pbkp_rediction times without worsening and even improving the existing accuracy results. The accuracy improvement is achieved by combining the numerical relevance of the ratings with non-numerical information based on the votes structure. The improvement of the pbkp_rediction time is achieved by setting four actions: 1) simplification of the similarity measure design, in order to minimize the necessary calculations; 2) construction and maintenance of a model that simplifies the pbkp_redictions processing; 3) optimization of the computation, using a set-based model and a bit-based processing implementation; and 4) switching between the bit processing and the numerical processing, depending on the density of the users' ratings. Experimental results show the improvements both in the pbkp_rediction time and the accuracy. Experiments have used a significant amount of state-of-the-art baselines and collaborative filtering public data sets.