Attribute-based collaborative filtering using genetic algorithm and weighted C-means algorithm

Recommender system technology can assist customers of a company to choose an appropriate product or service after learning their preferences. But this technology suffers from some problems such as scalability and sparsity. Since users express their opinions implicitly based on some specific attributes of items, this paper proposes a collaborative filtering algorithm based on attributes of items to address these problems. Attributes weight vector for each user is considered as a chromosome in genetic algorithm. This algorithm optimises the weights according to historical rating. A weighted C-means algorithm also is introduced to cluster users based on the optimised attributes weight vector. Finally, recommendation is generated by a user based similarity in each cluster. The experimental results show that our proposed method outperforms current algorithms and can perform superiorly and alleviates problems such as sparsity and precision quality. The main contribution of this paper is addressing sparsity problem using attribute weighting and scalability problem using weighted C-means algorithm.

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