Utility-based Recommender Systems Using Implicit Utility and Genetic Algorithm

This is the authors' abstract. We don't add key points for this paper.

Recommender systems are used to recommend items for user in e-commerce with information overload. Utility-based recommender systems build multi-attribute utility function of user and recommend the highest utility item for user. Some utility-based recommender systems use rating for items to extract utility function, which produce significant burden for user. The paper proposes a utility-based recommender technique which can predict attribute value utility and implicit holistic utility rate of items by user browsing behavior and genetic algorithm, and elicit the attribute weight by genetic algorithm, and building a multi-attribute utility function. The experimental results on clothing recommendation show that the proposed method is superior to current utility-based methods on accuracy, satisfaction, usefulness and time expense.