Improving the Attribute-Based Active Learning by Clustering the New Items
Junxin Zhou, Raja Chiky · 2019
The issue that recommender system often meets is cold-start problem, where the system does not have any ratings of new items or new users. Thus, it can not provide relevant recommendation for the new users or new items. In previous research, when dealing with item cold-start problem, some scientists combined content information and active learning method, and used factorization machine to model the prediction task. However, a shortcoming in this method is that when using factorization machine model to select users to give ratings to new items, the active users may be selected for too many times, leading to a result that they refuse to give ratings for new items, or randomly give their ratings, which does not exactly show their preferences. In this paper, to solve this issue, we use clustering algorithm to divide new items into different groups and choose one item to represent the group, and only request users giving ratings for the representative items.