Abstract
Collaborative filtering has been considered the most used approach for recommender systems in both practice and research. Unfortunately, traditional collaborative filtering suffers from the so-called cold-start problem, which is the challenge to recommend items for an unknown user. In this paper, we introduce a generic framework for social collective recommendations targeting to support and complement traditional recommender systems to achieve better results. Our framework is composed of three modules, namely, a User Clustering module, a Representative module, and an Adaption module. The User Clustering module aims to find groups of users, the Representative module is responsible for determining a representative of each group, and the Adaption module handles new users and assigns them appropriately. By the composition of the framework, the cold-start problem is alleviated.
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Acknowledgment
This research was partially supported by the Wrocław University of Science and Technology under Polish-German cooperation program between the Ministry of Science and Higher Education and the German Academic Exchange Service, Project No. 0401/0115/18; by statute research grant of Ministry of Science and Higher Education, Project No. 0402/0071/17; by DAAD under grant PPP 57391625; and by the Brazilian National Council for Scientific and Technological Development (CNPq) - Science without Borders Program.
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Homann, L., Maleszka, B., Martins, D.M.L., Vossen, G. (2018). A Generic Framework for Collaborative Filtering Based on Social Collective Recommendation. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11055. Springer, Cham. https://doi.org/10.1007/978-3-319-98443-8_22
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