Corpus ID: 18276949

Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews

@inproceedings{Tan2016RatingBoostedLT,
  title={Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews},
  author={Yunzhi Tan and Min Zhang and Yiqun Liu and Shaoping Ma},
  booktitle={IJCAI},
  year={2016}
}
The performance of a recommendation system relies heavily on the feedback of users. Most of the traditional recommendation algorithms based only on historical ratings will encounter several difficulties given the problem of data sparsity. Users' feedback usually contains rich textual reviews in addition to numerical ratings. In this paper, we exploit textual review information, as well as ratings, to model user preferences and item features in a shared topic space and subsequently introduce… Expand
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