New Recommendation Techniques for Multicriteria Rating Systems

By Gediminas Adomavicius, YoungOk Kwon. IEEE Intelligent Systems, 2007

551 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f311be6e576a91e45f90ad2d7b258643d7d9cad6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Hui Xiong
2. Feng Wang
3. Li Chen
4. Yong Ge
5. Zijun Yao
6. Yanjie Fu
7. Bin Liu
8. Guanliang Chen

In Information Systems

1. Feng Wang
2. Guanliang Chen
3. Hui Xiong
4. Li Chen
5. Yong Ge
6. Zijun Yao
7. Yanjie Fu
8. Bin Liu

In Management Science and Operations Research

1. Hui Xiong
2. Feng Wang
3. Li Chen
4. Yong Ge
5. Zijun Yao
6. Yanjie Fu
7. Bin Liu
8. Guanliang Chen

Citations in the corpus

• User Preference Learning with Multiple Information Fusion for Restaurant Recommendation (2014, methodology): “(5) Multi-criteria Collaborative Filtering (MCF) [1], which is delicately designed for multi-criteria (also called multi-rating or multi-aspect) recommendation.”
• Recommender Systems (2016, unknown): “The multi-criteria recommendation problem was first defined in the seminal work of [12].”
• Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews (2017, unknown): “[2]) and classify the ratings into “like” and “dislike”.”
• Recommender systems based on user reviews: the state of the art (2015, background): “The classical multi-criteria recommender system relies on user ratings for multiple aspects of an item (Adomavicius and Kwon 2007) to calculate the user-based or item-based similarity for CF (Tang and McCalla 2009).”