Konferensartikel

This paper proposes a model of user’s preference for retrieving preferred clothes. This paper has designed a decision process of user’s preference. This paper has made the user’s preference model based on indexes calculated in each step in the process. The process has three steps; i.e. Attention; Evaluation and Decision step. The attention step is that a user pays attention to principal features of clothes that is the features related to his/her interest. The attention step detects the principal features by the rough set and calculating Attention index. The attention index indicates the degree of user’s positive (or negative) attention to the principal features. The evaluation step is that a user evaluates interest concerning the principal features. The evaluation step estimates the preferred degree of the principal features of a user by Evaluation index. The evaluation index is calculated by unifying the attention indexes of positive and of negative. The decision step is that a user decides his/her preference for clothes by using his/her evaluation. The decision step estimates the user’s preference by Preference index that totalizes the evaluation index of the user. This paper has evaluated the estimation ability of user’s preference by the preference index. The result shows that the preference index could estimate the preferred feature. This paper also shows the result that has evaluated the recommendation of clothes by using the preferred feature to 9 users. The average of the rate of which the clothes that include the preferred features of the user have appeared in top 5 is 98 %.

User preference; User modeling; Personalization; Information retrieval; Rough set

Robin; B. (2002). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction; 12(4); 331-370.

Sung; Y; J.; Jeong-Hee; H. and Taek-Soo; K.; (2005). A Statistical Model for User Preference. IEEE Transactions on Knowledge and Data Engineering; 17(6); 834-843.

Fang; L.; Clement; Y. & Weiyi; M.; (2004). Personalized Web Search for Improving Retrieval Effectiveness. IEEE Transactions on Knowledge and Data engineering; 16(1); 28-40.

Ingrid Z.; & David W. A.;(2001). Predictive Statistical Models for User Modeling; User Modeling and User-Adapted Interaction; 11(1-2); 5-18.

Suk; J; Y.; Hyung; D; L.; Han; K; Y.; Hee Y; Y.; Ohyoung; S.; (2011). Personalized Recommendation System Reflecting User Preference with Context-Awareness for Mobile TV. 2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications Workshops; 232-237.

Lech; P.; (2002). Rough Sets; Advances in Intelligent and Soft Computing; Springer Science & Business.

Ning; S. and Wojclech; Z. (1995); Data-based acquisition and incremental modification of classification rules; Computational Intelligence; 11(2); 357-370.