Leveraging Item Connections to Improve Social Recommendations with Ratings and Reviews

@article{Huang2016LeveragingIC,
  title={Leveraging Item Connections to Improve Social Recommendations with Ratings and Reviews},
  author={Jiajin Huang and Ning Zhong},
  journal={2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)},
  year={2016},
  pages={185-191}
}
Recommender systems aim to provide users with preferred items to tackle the information overload problem in the Web era. Social relations, item connections, and usergenerated reviews on items contain abundant potential information. By combining matrix factorization with latent Dirichlet allocation, we integrate ratings, reviews, user similarity and item similarity in recommender systems. The experimental result on a real-world dataset proves that both item connection and user connection contain… CONTINUE READING

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