Virtual information core optimization for collaborative filtering recommendation based on clustering and evolutionary algorithms

Collaborative filtering (CF), the most widely used recommendation algorithm, has to face the sparsity and scalability problem. Some researchers proposed to select a representative set of real users called information core (IC) from all the real users, which is used as the candidate neighbor set in the CF to alleviate the scalability problem. However, the rating vectors of these real users that compose IC are usually sparse, which will negatively affect the recommendation accuracy. In this paper, a virtual information core (VIC) optimization algorithm is proposed based on clustering and evolutionary algorithms for CF recommendation (VICO-CEA). The problem of searching for VIC is modeled as a combinatorial optimization problem, and is solved offline by the proposed evolutionary algorithm. The VIC is a set of virtual core users, each of which is constructed by averaging out multiple real users. These virtual core users in the VIC are no longer sparse and are found by the evolutionary optimization, which will improve the recommendation accuracy and reduce the online recommendation time as the VIC is used as the candidate neighbor set in the CF. Meanwhile, to make offline optimization more efficient, two strategies are proposed. One is to design a simple similarity measure based on dimensionality reduction and clustering to save time in calculating similarities by reducing the dimensionality of users' rating vectors. The other is to use dimensionality reduction and clustering to construct a smaller training set and validation set by reducing the dimensionality of items' rating vectors. The experimental results show that VICO-CEA can not only significantly reduce the online recommendation time further but also improve the recommendation accuracy greatly compared to traditional CF and other information-core-based methods. (C) 2021 Elsevier B.V. All rights reserved.