Recommender systems have become more important in various domains for lessening the issue of information overload. Traditional Recommender Systems are Collaborative filtering method and Content based filtering method.However, these recommendation methods suffer from data sparsity and cold start problem. So this paper proposes an ANN based recommender system. Artificial Neural Network –Learning Vector Quantization (ANNLVQ) and Optimized Learning Vector Quantization (ANNOLVQ) algorithms are used todevelop a multi-categorical classification model that predicts the class of a rating in recommender systems. In this proposed research, the problem of predicting the rating as a multi-label classification problem is considered where each rating has treated a label. Book dataset used for this proposed research. ANN recommender systems accuracy compared with collaborative filtering method recommender system and ANN recommender systems predicts more accuracy than traditional collaborative filtering method.

Artificial Neural Network, collaborative filtering, Learning Vector Quantization, Book Recommendation, Recommender systems.

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