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http://dx.doi.org/10.3745/KIPSTD.2009.16-D.2.273

Weight Based Technique For Improvement Of New User Recommendation Performance  

Cho, Sun-Hoon (한국원자력연구소 하나로운영부)
Lee, Moo-Hun (한남대학교 컴퓨터공학과)
Kim, Jeong-Seok (한남대학교 컴퓨터공학과)
Kim, Bong-Hoi ((주)유비엔씨)
Choi, Eui-In (한남대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartD / v.16D, no.2, 2009 , pp. 273-280 More about this Journal
Abstract
Today, many services and products that used to be only provided on offline have been being provided on the web according to the improvement of computing environment and the activation of web usage. These web-based services and products tend to be provided to customer by customer's preferences. This paradigm that considers customer's opinions and features in selecting is called personalization. The related research field is a recommendation. And this recommendation is performed by recommender system. Generally the recommendation is made from the preferences and tastes of customers. And recommender system provides this recommendation to user. However, the recommendation techniques have a couple of problems; they do not provide suitable recommendation to new users and also are limited to computing space that they generate recommendations which is dependent on ratings of products by users. Those problems has gathered some continuous interest from the recommendation field. In the case of new users, so similar users can't be classified because in the case of new users there is no rating created by new users. The problem of the limitation of the recommendation space is not easy to access because it is related to moneywise that the cost will be increasing rapidly when there is an addition to the dimension of recommendation. Therefore, I propose the solution of the recommendation problem of new user and the usage of item quality as weight to improve the accuracy of recommendation in this paper.
Keywords
Recommendation; Cold Start; Collaborative Filtering; Hybrid Filtering; Weight;
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