The comparison of several methods of processing no-rated items in collaborative filtering algorithm
Zhang Jinbo, Zhiqing Lin, Bo Justin Xiao, Chuang Zhang · 2009
Collaborative Filtering is a very important technology in E-commerce. Unfortunately, with the increase of users and commodities, the user rating data is extremely sparse, which leads to the low efficient Collaborative Filtering recommendation system. To address these issues, many methods of processing no-rated items in Collaborative Filtering recommendation algorithm have been proposed, including algorithm without taking the no-rated items into account, and algorithms setting the ratings of no-rated items' value as 0, half of the full score, average of the target item's rating score, or the average of the target user's rating score. This paper compares the several methods, and the experimental results show that the method of set ting no-rated items' value as 0 is the best method in these methods.