Are Features Equally Representative? A Feature-Centric Recommendation


  • Chenyi Zhang Zhejiang University
  • Ke Wang Simon Fraser University
  • Ee-peng Lim Singapore Management University
  • Qinneng Xu City University of Hong kong
  • Jianling Sun Zhejiang University
  • Hongkun Yu University of Illinois at Urbana-Champaign



Typically a user prefers an item (e.g., a movie) because she likes certain features of the item (e.g., director, genre, producer). This observation motivates us to consider a feature-centric recommendation approach to item recommendation: instead of directly predicting the rating on items, we predict the rating on the features of items, and use such ratings to derive the rating on an item. This approach offers several advantages over the traditional item-centric approach: it incorporates more information about why a user chooses an item, it generalizes better due to the denser feature rating data, it explains the prediction of item ratings through the predicted feature ratings. Another contribution is turning a principled item-centric solution into a feature-centric solution, instead of inventing a new algorithm that is feature-centric. This approach maximally leverages previous research. We demonstrate this approach by turning the traditional item-centric latent factor model into a feature-centric solution and demonstrate its superiority over item-centric approaches.




How to Cite

Zhang, C., Wang, K., Lim, E.- peng, Xu, Q., Sun, J., & Yu, H. (2015). Are Features Equally Representative? A Feature-Centric Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).