Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction

By Longke Hu, Aixin Sun, Yong Liu. 2014

177 citations recorded by the source corpus.

Subfields: Information Systems, Marketing, Transportation

How researchers see this paper

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

In Information Systems

1. Xiangnan He
2. Tat-seng Chua
3. Liqiang Nie
4. Xiang Wang
5. Hanwang Zhang
6. Fuli Feng
7. Lizi Liao
8. Yongfeng Zhang

In Marketing

1. Liqiang Nie
2. Tat-seng Chua
3. Jia-Dong Zhang
4. Lizi Liao
5. Chi-Yin Chow
6. Hanwang Zhang
7. Xiangnan He

In Transportation

1. Tat-seng Chua
2. Jia-Dong Zhang
3. Chi-Yin Chow
4. Hanwang Zhang
5. Xiangnan He

Citations in the corpus

• Neural Collaborative Filtering (2017, background): “This paper explores the use of deep neural networks for learning the interaction function from data, rather than a handcraft that has been done by many previous work [18, 21].”
• GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations (2015, methodology): “Our method is different from the existing works [1, 8, 15, 18, 27, 35] that separately utilize the category and/or popularity information of POIs.”
• Exploiting Geographical Neighborhood Characteristics for Location Recommendation (2014, unknown): “, business rating prediction [7].”