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Enhancing memory-based collaborative filtering for group recommender systems
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DOI：10.1016/j.eswa.2014.11.042.png)
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Abstract
En 中文
Memory-based collaborating filtering techniques are widely used in recommender systems. They are based on full initial ratings in a user-item matrix. However, most of the time in group recommender systems, this matrix is sparse and users' preferences are unknown. This deficiency may make memory-based collaborative filtering unsuitable for group recommender systems. This paper, improves memory-based techniques for group recommendation systems by resolving the data sparsity problem. The core of the proposed method is based on a support vector machine learning model that computes similarities between items. This method employs calculated similarities and enhances basic memory-based techniques. Experiments demonstrate that the proposed method overcomes the memory-based techniques. It also indicates that the presented work outperforms the latent factor approach, which is very efficient in sparse conditions. Finally, it is indicated that the proposed method gives a better performance than existing approaches on generating group recommendations. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords:
Group recommendation system
Sparsity problem
Collaborative filtering technique
User-based approach
Item-based approach
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