Aspect-based latent factor model by integrating ratings and reviews for recommender system
By Lin Qiu, Sheng Gao, Wenlong Cheng, Jun Guo. 2016
64 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Alexander Tuzhilin
- Konstantin Bauman
- Zhiyong Cheng
- Ying Ding
- Xiaojun Chang
- Lei Zhu
- B. Liu
- Mohan Kankanhalli
In Information Systems
- Zhiyong Cheng
- Ying Ding
- Xiaojun Chang
- Lei Zhu
- B. Liu
- Alexander Tuzhilin
- Mohan Kankanhalli
- Rose Catherine
Citations in the corpus
- MMALFM (2018, methodology): “A general approach of these methods is to extract latent topics from reviews using topic models [41, 45, 56, 58, 63] or non-negative MF [2, 52] and learn latent factors from ratings using MF methods.”
- Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews (2018, methodology): “A general approach of these methods is to extract latent topics from reviews using topic models [23, 26, 31, 32, 39] or non-negative MF [2, 29] and learn latent factors from ratings using MF methods.”
- Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews (2017, background): “In [22] the authors presented Aspectbased Latent Factor Model (ALFM) that combines ratings and review texts to improve rating predictions.”