关键词: 推荐系统, 冷启动, 多臂赌博机, 协同过滤

Abstract: With the wide application of the recommender system in various domains, the cold start problem has gained increasing attention in recent years. For the situation where only text comments of items are available, this paper proposes a set of solution and algorithm that can solve the cold start problem of new users. Firstly, the similarity between the article topic extraction and the feature extraction based on commodity evaluation is found and the latent Dirichlet allocation model is used to extract the features of items innovatively. Then the COLINBA algorithm is proposed which introduces the synergies of neighboring users based on the traditional Bandits strategy. This algorithm adds the neighbors’ similarity weight coefficient which determines the influence degree of the neighbors on the target user and makes the synergy more accurate and effective. The user features are updated according to the feedback of the user and the recommended item features. Finally, based on the two real world datasets, Delicious and Last.fm, this paper experimentally compares COLINBA to the state-of-the-art methods, and the results show that COLINBA offers a significant increase in recommendation performance.

Key words: recommender system, cold start, Bandits, collaborative filtering