Affiliations: [a] School of Computing, Engineering and Mathematics, University of Brighton, Brighton, United Kingdom
| [b] Department of Software Engineering and Artificial Intelligence, Complutense University, Madrid, Spain
| [c]
Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom
| [d]
Department of Computer Science, Middlesex University, London, United Kingdom
Correspondence:
[*]
Corresponding author. Nikolaos Polatidis, School of Computing, Engineering and Mathematics, University of Brighton, Lewes Road, BN2 4GJ, Brighton, United Kingdom. E-mail: [email protected].
Abstract: Recommender systems are decision support systems that play an important part in generating a list of product or service recommendations for users based on the past experiences and interactions. The most popular recommendation method is Collaborative Filtering (CF) that is based on the users’ rating history to generate the recommendation. Although, recommender systems have been applied successfully in different areas such as e-Commerce and Social Networks, the popularity bias is still one of the challenges that needs to be further researched. Therefore, we propose a multi-level method that is based on a switching approach which solves the long tail recommendation problem (LTRP) when CF fails to find the target case. We have evaluated our method using two public datasets and the results show that it outperforms a number of bases lines and state-of-the-art alternatives with a further reduce of the recommendation error rates for items found in the long tail.
