Location recommendation for location-based social networks

By Mao Ye, Peifeng Yin, Wang-Chien Lee. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2010

534 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Transportation

View Paper: https://www.semanticscholar.org/paper/790f17902a0f58a06d69cbadc44216d0a8700b27

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. Huan Liu
3. Jiliang Tang
4. Gao Cong
5. Xia Hu
6. Huiji Gao
7. Quan Yuan
8. Michael R. Lyu

In Sociology and Political Science

1. Xia Hu
2. Huan Liu
3. Jiliang Tang
4. Huiji Gao
5. Mohamed F. Mokbel
6. Yu Zheng
7. Jie Bao
8. Gao Cong

In Transportation

1. Yu Zheng
2. Gao Cong
3. Quan Yuan
4. Jie Bao
5. Mohamed F. Mokbel
6. Michael R. Lyu
7. Aixin Sun
8. David Wilkie

Citations in the corpus

• Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks (2012, background): “The other line of work centers on POI recommendation based on the LBSNs data (Ye, Yin, and Lee 2010; Ye et al. 2011).”
• A new user similarity model to improve the accuracy of collaborative filtering (2014, methodology): “Collaborative filtering (CF), as a kind of personalized recommendation technique, has been widely used in many domains [1–3, 12, 13 ].”
• LCARS: a location-content-aware recommender system (2013, background): “Specifically, [14, 30, 24] deposited people’s activity history into user-venue matrix where each row corresponds to a user’s venue-visiting history and each column denotes a venue such as a restaurant.”
• Content-Aware Point of Interest Recommendation on Location-Based Social Networks (2015, background): “Ye et al. (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.”
• 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.”
• Exploring temporal effects for location recommendation on location-based social networks (2013, methodology): “To benefit LBSN users and advance location-based marketing, location recommendation on LBSNs has become an essential task [24, 25], aiming to recommend new POIs (Points of Interest) to a user according to his personal preferences and facilitate his exploration of new areas of the city.”
• A location-based news article recommendation with explicit localized semantic analysis (2013, unknown): “Ye et al. proposed Geo-Measured Friend-based Collaborative Filtering (GM-FCF) to recommend places such as stores, movie theaters, etc [24].”
• Exploiting geographical influence for collaborative point-of-interest recommendation (2011, background): “[22] is the first research to provide location recommendations services in LBSNs, but with the goal of improving efficiency of location recommendation.”
• Recommendations in location-based social networks: a survey (2015, methodology): “, [20, 25, 45, 111], provide location recommendations based on the distribution of user’s ratings over their visited locations using the above equation.”
• Where You Like to Go Next: Successive Point-of-Interest Recommendation (2013, unknown): “The other line of work focuses on LBSN data, which is very sparse and large-scale [Ye et al. , 2010; 2011; Cheng et al. , 2012].”
• Exploiting Geographical Neighborhood Characteristics for Location Recommendation (2014, background): “In previous work [26,27], the user-based CF has been successfully applied for the location recommendation tasks, and it has been found to significantly outperform item-based CF. Similar results have also been found in our experiments (see Section 5.2.1).”
• Exploring social influence for recommendation: a generative model approach (2012, background): “As reported in the literature, social friendship is shown to be beneficial for recommendation [19, 18, 17, 12, 16, 32, 31, 6].”
• Time-aware point-of-interest recommendation (2013, background): “In their earlier work [22], Ye et al. aim to improve recommendation e ffi ciency for POI recommendations in LBSNs by exploiting the social and geospatial information in recommendation.”