利用Tri-training算法解决推荐系统冷启动问题

Utilizing Tri-training Algorithm to Solve Cold Start Problem in Recommender System

随着社交网络的发展,推荐系统日趋重要,而冷启动问题是推荐系统中的关键问题。设计了一种基于上下文的半监督学习框架TSEL,对矩阵分解模型SVD进行扩充以支持更多形式的上下文信息,利用Tri-training框架训练各个模型。与其他解决推荐系统冷启动问题的半监督方法(如Co-training)相比,该方法有着更好的效果。Tri-training框架能够更加方便地引入更多推荐模型,具有更好的可扩展性。将Tri-training框架加以扩展,提出了基于用户活跃度生成无标记教学集合的算法和更加丰富的对矩阵分解模型扩充的形式。在真实数据集MovieLens上进行验证,获得了更好的实验效果。

With the development of social network, recommender system is becoming more and more important. Cold start is one of the most important problems in recommender system. A context-based semi-supervised learning frame- work TSEL was designed. We expanded matrix faetorization model SVD to support more kinds of context information, and used Tri-training framework to train individual models. Compared with other methods which solve cold start prob- lems in recommender system （e. g. Co-training）, our algorithm has better performance. Tri-training framework can in- corporate more recommender models and has good expansibility. We expanded Tri-training framework, and proposed a user activeness-based unlabeled teaching set generating algorithm. We proposed more kinds of models which expand the matrix factorization. We evaluated our algorithm on real world dataset, i. e. MovieLens, and got better performance.