Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, with the continuous growth of online user reviews, the information available for training a recommender system is no longer limited to just numerical star ratings or user/item features. By extracting explicit user opinions about various aspects of a product from the reviews, it is possible to learn more details about what aspects a user cares, which further sheds light on the possibility to make explainable recommendations.

• NF
  National Foundation for Science and Technology Development
• NN
  National Natural Science Foundation of China

  Awards: 61073071, 60903107
• MO
  Ministry of Science and Technology of the People's Republic of China

  Award: (2011AA01 A205
• DO
  Division of Information and Intelligent Systems

  Award: IIS-0713111