Development of Recommender Systems Using User Preference Tendencies: An Algorithm for Diversifying Recommendation

Abstract. Many e-commerce sites use a recommendation system to filter the specific information that a user wants out of an overload of information. Currently, the usefulness of the recommendation is defined by its accuracy. However, findings that users are not satisfied only with accuracy have been reported. We consider that a recommendation having only accuracy is unsatisfactory. For this reason, we define the usefulness of a recommendation as its ability to recommend an item that the user does not know, but may like. To improve user satisfaction levels with recommendation lists, we propose an alternative recommendation algorithm that increases the diversity of the recommended items. We examined items that appeal to several different taste tendencies to create a list and achieved diversity in that list. First, we created a similarity network of items by using item rating data. Second, we clustered the items in the network and identified the topics that appealed to the same preference ...

• Type: Conference
• Year: 2008
• Where: I3E
• Authors: Yuki Ogawa, Hirohiko Suwa, Hitoshi Yamamoto, Isamu Okada, Toshizumi Ohta
• Keywords: Alternative Recommendation Algorithm, Business, I3E 2008, Recommendation List, User Satisfaction Levels