Sciweavers

Share
I3E
2008

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

9 years 9 months ago
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 ...
Yuki Ogawa, Hirohiko Suwa, Hitoshi Yamamoto, Isamu
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where I3E
Authors Yuki Ogawa, Hirohiko Suwa, Hitoshi Yamamoto, Isamu Okada, Toshizumi Ohta
Comments (0)
books