Exploiting geographical influence for collaborative point-of-interest recommendation

By Mao Ye, Peifeng Yin, Wang-Chien Lee, Dik Lun Lee. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2011

1,157 citations recorded by the source corpus.

Subfields: Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/d2e7d37dac6b9eb313bd7918f162485111608bea

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. Yu Zheng
2. Jia-Dong Zhang
3. Chi-Yin Chow
4. Huan Liu
5. Gao Cong
6. Philip S. Yu
7. Charu C. Aggarwal
8. Jiliang Tang

In Transportation

1. Yu Zheng
2. Jia-Dong Zhang
3. Chi-Yin Chow
4. Gao Cong
5. Xing Xie
6. Jie Bao
7. Mohamed F. Mokbel
8. Ouri Wolfson

Citations in the corpus

• Urban Computing: Concepts, Methodologies, and Applications (2014, positive): “In the dynamic taxi ridesharing system T-Share, Ma et al. [2013] combined spatiotempo­ral database techniques with optimization algorithms to signi.cantly scale down the number of taxis that needs to be checked.”
• Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation (2020, background): “Early studies in POI recommendation focus mainly on estimating users’ preferences using Collaborative Filtering (CF), especially Matrix Factorization (MF) based techniques (Ye et al. 2011; Cheng et al. 2012; Lian et al. 2014; Gao et al. 2015).”
• Location-based and preference-aware recommendation using sparse geo-social networking data (2012, background): “Specifically, [6, 14, 23, 22] deposit people’s location histories into a user-location matrix where a row corresponds to a user’s location history and each column denotes a venue like a restaurant.”
• Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction (2014, background): “Moreover, POI recommendation has also considered social influence among friends [25].”
• GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations (2015, methodology): “This method [23] linearly integrates user preference from user-based collaborative filtering, social influence from social collaborative filtering, and geographical influence from a common distance distribution for all users, but it does not take into account the category and popularity information ”
• Personalized Ranking Metric Embedding for Next New POI Recommendation (2015, methodology): “We exploit two wellknown measure metrics [Yuan et al., 2013], namely Precision@N and Recall@N (denoted by Pre@N and Rec@N respectively ).”
• Practical Privacy Preserving POI Recommendation (2020, methodology): “Therefore, we build the user adjacent network by using user geographical information, similar to the existing research [13, 63].”
• User Preference Learning with Multiple Information Fusion for Restaurant Recommendation (2014, methodology): “For example, previous works [24, 22] applied the collaborative filtering method to recommend locations based on trajectory data or check-in data.”
• Time-aware point-of-interest recommendation (2013, methodology): “Because of the different assumptions made in [23] and our work, the techniques are significantly different.”
• GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation (2014, methodology): “[24] characterized it by a power law distribution on the distance between any pair of visited locations.”
• Recommendations in location-based social networks: a survey (2015, methodology): “2011 [112], use user check-ins to study the effects of the CF-model, geographical distance, and social structures in making location recommendations.”
• Exploring social influence for recommendation: a generative model approach (2012, methodology): “For example, the reported precision in [32] is 0.”
• Content-Aware Point of Interest Recommendation on Location-Based Social Networks (2015, background): “(Ye, Yin, and Lee 2010) introduced POI recommendation into LBSNs and investigated the geographical influence (Ye et al. 2011) and social influence (Ye, Liu, and Lee 2012) for POI recommendation.”
• Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (2017, background): “…small • IRenMF [28]: it models geographical influence by incorporating • LOCABAL [38]: it models two social relations: social friends and • USG [47]: it combines geographical influence, social network and • iGSLR [52]: it exploits personalized geographical preference and • LORE [54]: it…”
• Exploring temporal effects for location recommendation on location-based social networks (2013, background): “Specifically, the authors investigated the geographical influence [25] and social influence [23] for location recommendation, and discovered that user preference plays a more important role in contributing to the recommendation than social and geographical influence.”
• LCARS: a location-content-aware recommender system (2013, background): “• User interest, social and geographical influences (USG): Following recent location-based recommendation work [31],”