A hybrid recommendation approach based on attributes of products using genetic algorithm and naive Bayes classifier

Recommender system technology can present personalised offers to customers of companies. This technology suffers from the cold-start and sparsity problems. On the other hand, in most researches, less attention has been paid to user's preferences varieties in different product categories and also explicit and implicit attributes of products. Since users express their opinions implicitly based on some specific attributes of products, this paper proposes a hybrid recommendation approach based on attributes of products to address these problems. After product category and taxonomy formation and attributes extraction for each category, explicit-based module provides recommendations through naive Bayes classifier. Implicit-based module considers the weight vector of implicit attributes for users as chromosomes in genetic algorithm. This algorithm optimises the weights according to historical rating. Finally, recommendations are generated using the results of two modules. The main contributions are addressing sparsity and cold-start problem using naive Bayes classifier and weight optimisation by genetic algorithm.