Recommending friends and locations based on individual location history

By Yu Zheng, Lizhu Zhang, Zhengxin Ma, Xing Xie, Wei-Ying Ma. ACM Transactions on the Web, 2011

530 citations recorded by the source corpus.

Subfields: Information Systems, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5ede3ba5fd07d243832db4d63ff656b58afd419d

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. Xia Hu
5. Michael R. Lyu
6. Irwin King
7. Huiji Gao
8. Jie Bao

In Transportation

1. Yu Zheng
2. Xing Xie
3. Ouri Wolfson
4. Jie Bao
5. Mohamed F. Mokbel
6. Michael R. Lyu
7. Licia Capra
8. David Wilkie

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Jie Bao
4. Ouri Wolfson
5. Mohamed F. Mokbel
6. Licia Capra
7. David Wilkie
8. Guangzhong Sun

Citations in the corpus

• GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory (2010, unknown): “Refer to paper [10] for details.”
• Location-based and preference-aware recommendation using sparse geo-social networking data (2012, background): “[28] proposed a hierarchical-graph-based similarity measurem ent taking the human mobility features into account.”
• Trajectory Data Mining (2015, methodology): “…P into a sequence of meaningful places S , therefore facilitating a diversity of applications, such as travel recommendations [Zheng and Xie 2011b; Zheng et al. 2011c], destination prediction [Ye et al. 2009], taxi recommendation [Yuan et al. 2011b, 2013b], and gas consumption estimation [Zhang…”
• Methodologies for Cross-Domain Data Fusion: An Overview (2015, methodology): “For example, user p‘s interest (rpi) in a location i can be predicted according to Equation (1), which is an implementation of user-based collaborative filtering [45], [91]:”
• Recommendations in location-based social networks: a survey (2015, background): “2011 [134] further extends the user similarity measure framework presented in [56] by considering the sequences of locations at different spatial granularities.”
• Mobile Crowd Sensing and Computing (2015, methodology): “…phone users to share local knowledge (e.g., local information, ambient context, noise level, and traffic conditions) acquired by their sensor-enhanced devices, and the information can be further aggregated in the cloud for large-scale sensing and community intelligence mining [Zhang et al. 2011].”
• Urban Computing: Concepts, Methodologies, and Applications (2014, methodology): “Y. Zheng, Y. Chen, X. Xie, and W. Y. Ma. 2009a. GeoLife2.”
• Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks (2012, background): “One line of research is to solve POI recommendation based on the extracted stay points from GPS trajectory logs of several hundred monitored users (Zheng et al. 2009; 2010a; 2010b; Leung, Lee, and Lee 2011; Zheng and Xie 2011; Cao, Cong, and Jensen 2010).”
• Exploring temporal effects for location recommendation on location-based social networks (2013, background): “In previous work researchers explored users’ personal static check-in preferences through geographical check-ins for location recommendation [28, 1, 29, 26].”
• GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation (2014, background): “Zheng et al. [28] designed a random walk style model to do tourism hot spot recommendation by taking into account both users’ travel experiences and location attractiveness.”
• Deep reinforcement learning for page-wise recommendations (2018, background): “Recommender systems have become increasingly popular in recent years, and have been utilized in a variety of domains including movies, music, locations, and social events [2, 11, 13, 30, 31, 39, 41, 45].”
• Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics" (2012, methodology): “One obvious use of the clusters discovered would be to design a recommendation system, as is done in [27] with the user similarity metric developed first in [10].”
• Recommender systems survey (2013, background): “The travel GPS traces can be reinforced with social information based on friends [250].”
• Where You Like to Go Next: Successive Point-of-Interest Recommendation (2013, unknown): “…social networks have received much attention in recent years due to the new characteristics of spatial-temporal-social information embedded in the check-in data and the prevalence of various interesting real-world applications [Yuan et al. , 2012; Zheng et al. , 2011; Cheng et al. , 2011a].”