Learning geographical preferences for point-of-interest recommendation

By Bin Liu, Yanjie Fu, Zijun Yao, Hui Xiong. Knowledge Discovery and Data Mining, 2013

447 citations recorded by the source corpus.

Subfields: Information Systems, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a91c22f5bfa77822c53858ceaeb8b3bd71dbc263

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. Jia-Dong Zhang
2. Chi-Yin Chow
3. Yu Zheng
4. Qiang Yang
5. Xing Xie
6. Xia Hu
7. Huan Liu
8. Jiliang Tang

In Transportation

1. Yu Zheng
2. Jia-Dong Zhang
3. Chi-Yin Chow
4. Xing Xie
5. Jie Bao
6. Mohamed F. Mokbel
7. David Wilkie
8. Qiang Yang

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Chi-Yin Chow
4. Jia-Dong Zhang
5. Jie Bao
6. Mohamed F. Mokbel
7. Qiang Yang
8. David Wilkie

Citations in the corpus

• Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation (2015, methodology): “By following previous studies [22, 3, 12], we set wll′ = (0.”
• Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction (2014, background): “Regional popularity is mentioned as one of the unique characteristics in LBSNs which distinguish POI recommendation from other recommendation tasks [15].”
• Recommendations in location-based social networks: a survey (2015, background): “6 An overview of geographical preference model for POI recommendation [62]”
• Exploiting Geographical Neighborhood Characteristics for Location Recommendation (2014, methodology): “In [17], Liu et al. introduced a probabilistic recommendation framework that employs the geographical influence on a user’s check-in behavior, the user mobility pattern and the user check-in count data for location recommendation.”
• SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews (2015, background): “Each latent region is modeled as a Gaussian distribution in their model.”
• GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations (2015, methodology): “…employed the prior preference of users to unvisited POIs as the weight of the edges between the users and POIs in the complete bipartite graph, while other studies utilize the prior preference to adjust the posterior preference that are derived from the geographical information [8, 13, 14, 28].”
• GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation (2014, methodology): “This algorithm has been exploited in [2, 12] for POI recommendation.”
• CoNet: Collaborative Cross Networks for Cross-Domain Recommendation (2018, background): “Latent factor models learn feature vectors for users and items mainly based on matrix factorization (MF) [20] which has probabilistic interpretations [24, 28].”
• Content-Aware Point of Interest Recommendation on Location-Based Social Networks (2015, background): “Similar performance can also be observed in other POI recommendation work (Liu et al. 2013; Yin et al. 2013; Hu and Ester 2013).”
• User Preference Learning with Multiple Information Fusion for Restaurant Recommendation (2014, background): “More recently, the work [7, 16] considered the multicenter of user check-in patterns, user mobility, and incorporated the inverse effect of geographic proximity as well as social influence for POI recommendation.”