Computer Science ›› 2016, Vol. 43 ›› Issue (12): 108-114.doi: 10.11896/j.issn.1002-137X.2016.12.019

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

ZHANG Xu-chen   

  • Online:2018-12-01 Published:2018-12-01

Abstract: 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 framework TSEL was designed.We expanded matrix factorization 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 problems in recommender system (e.g.Co-training),our algorithm has better performance.Tri-training framework can incorporate 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.

Key words: Recommender system,Machine learning,Tri-training

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