A Weighted Distance Similarity Model with Profile Expansion to Improve the Accuracy of Collaborative Recommender Systems

• Main Authors: Bing-Hao Huang, 黃炳豪
• Other Authors: Bi-ru Dai
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/96610492406167816884

碩士 === 國立臺灣科技大學 === 資訊工程系 === 103 === Collaborative filtering is one of the most widely used methods to provide product recommendation in online stores. The key component of the method is to find similar users or items by using user-item matrix so that products can be recommended based on the similarities. However, traditional collaborative filtering approaches compute the similarity between a target user and the other user without considering a target item. More specifically, they give an equal weight to each of the items which are rated by both users. However, we think that the similarity between the target item and each of the co-rated items is a very important factor when we calculate the similarity between two users. Therefore, in this paper we propose a new similarity function that takes similarities between a target item and each of the co-rated items and the proportion of common ratings into account. In addition, we also combine the item genre to the profile expansion to reinforce our model in order to deal with the cold-start problem. Experimental results from MovieLens dataset show that the method improves accuracy of recommender system significantly.