Abstract-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. Keywords-recommended system; multi-attribute utility; implicit utility; genetic algorithm; browsing behavior Recommender systems can help customers search for the products which they like in e-commerce context. Utility-based recommender systems can extract the decision maker's utility function, and calculate the utility of each item, and recommend high-utility items to users [1, 2] . Utility-based recommender systems can improve rating sparsity, new items, new users problems that collaborative filtering recommender technique encounters [3] , and it outperforms the traditional content-based recommender systems [1] . The central technique of utility-based recommender systems is constructing utility function. Much utility function is based on multi-attribute utility theory (MAUT). A customer makes buying decision based on multiple decision attribute of products. MAUT uses multi-attribute utility function to model customer preference, and maximize utility function to look for the best decision on the basis of multi-criteria. Some utility function extraction techniques are able to come to high decision accuracy, such as simple multi-attribute rating technique exploiting ranks (SMARTER), radial basis function network (RBFN) [4] , regression analysis [5, 6] , and neural network. However, these methods require user rating and increase user effort. This paper designs a utility function extraction technique which is based on MAUT and use implicit utility from user browsing behaviors data [7] and genetic algorithm (GA) [8, 9] , and which is called IU-GA. A utility-based recommender system is proposed based on IU-GA which can classify item data based on central item attributes, and calculate utility of items with user preferring attributes and recommend top-N utility items to user. This section briefly introduces utility-based recommender systems, MAUT, and implicit utility elicitation methods. Utility-based recommender systems make recommendations based on the calculation of the utility of each item for the user. Utility-based recommender techniques elicit multi-attribute utility function based on item rates that user offer to describe user preferences, and apply the utility function to calculate item utility for user [3] . Utility-based recommendations have not some problems of collaborative filtering recommender technique, such as cold-start, sparsity, and high dimensions. However, utility-based recommender systems face eliciting utility function for each user, which require remarkable burden of interaction. Several utility-based recommender systems have been developed based on user rating for items [4] [5] [6] 10, 11] . They are based on MAUT and needs significant user effort. So, modeling utility function with little user effort is a critical issue in designing utility-based recommender systems. MAUT is a quantitative and systematical method to describe use making decision. MAUT can model user making-decision involving multiple interdependent objectives on the basis of uncertainty and preference analysis. A MAU function based on MAUT can be generally represented by: International Conference on Mechatronics, Electronic, Industrial and Control Engineering (MEIC 2015)
The development of a utility-based recommender system to deal with clothing recommendations is presented in REF .
Utility-based Recommender Systems Using Implicit Utility and Genetic Algorithm
ICM 2015
Computer Science