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Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review

Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review

Lediona Nishani, Marenglen Biba
Copyright: © 2017 |Pages: 20
ISBN13: 9781522504894|ISBN10: 1522504893|EISBN13: 9781522504900
DOI: 10.4018/978-1-5225-0489-4.ch014
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MLA

Nishani, Lediona, and Marenglen Biba. "Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review." Collaborative Filtering Using Data Mining and Analysis, edited by Vishal Bhatnagar, IGI Global, 2017, pp. 250-269. https://doi.org/10.4018/978-1-5225-0489-4.ch014

APA

Nishani, L. & Biba, M. (2017). Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review. In V. Bhatnagar (Ed.), Collaborative Filtering Using Data Mining and Analysis (pp. 250-269). IGI Global. https://doi.org/10.4018/978-1-5225-0489-4.ch014

Chicago

Nishani, Lediona, and Marenglen Biba. "Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review." In Collaborative Filtering Using Data Mining and Analysis, edited by Vishal Bhatnagar, 250-269. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0489-4.ch014

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Abstract

People nowadays base their behavior by making choices through word of mouth, media, public opinion, surveys, etc. One of the most prominent techniques of recommender systems is Collaborative filtering (CF), which utilizes the known preferences of several users to develop recommendation for other users. CF can introduce limitations like new-item problem, new-user problem or data sparsity, which can be mitigated by employing Statistical Relational Learning (SRLs). This review chapter presents a comprehensive scientific survey from the basic and traditional techniques to the-state-of-the-art of SRL algorithms implemented for collaborative filtering issues. Authors provide a comprehensive review of SRL for CF tasks and demonstrate strong evidence that SRL can be successfully implemented in the recommender systems domain. Finally, the chapter is concluded with a summarization of the key issues that SRLs tackle in the collaborative filtering area and suggest further open issues in order to advance in this field of research.

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