Probabilistic Attribute Mapping for Cold-Start Recommendation

  • Guangxin Wang
  • Yinglin Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 213)


Collaborative filtering recommender system performs well when there are enough historical data of the users’ online behavior, but it does not work on new users who have not rated any items, or new items that have not been rated by any users, which are called cold-start user and cold-start item, respectively. In order to alleviate the cold-start problem, additional information such as the attributes of users and items must be used. We propose a novel hybrid recommender system, which tries to construct the probabilistic relationship between user attributes and movie attributes using EM algorithm. It can make recommendation for both new users and new items. We evaluate our approach on MovieLens dataset and compare our method with the state-of-the-art approach. Experimental results show that the two approaches have almost the same performance, while our approach uses less time to train the model and make online recommendation.


Recommender system Cold start User and item features Latent variable model 



This paper was supported by the Science and Technology Innovation Action Plan (Grant Number:12511502902) of Shanghai Science and Technology Committee.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringShanghai JiaoTong UniversityShanghaiChina

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