Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
1-2015
Abstract
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.
Discipline
Computer Sciences | Databases and Information Systems
Publication
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence: January 25-30, Austin, Texas
First Page
389
Last Page
395
ISBN
9781577356981
Publisher
AAAI Press
City or Country
Palo Alto, CA
Citation
ZHANG, Chenyi; WANG, Ke; Ee-peng LIM; XU, Qinneng; SUN, Jianling; and YU, Hongkun.
Are features equally representative? A feature-centric recommendation. (2015). Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence: January 25-30, Austin, Texas. 389-395.
Available at: https://ink.library.smu.edu.sg/sis_research/3102
Copyright Owner and License
LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.