Exploring temporal effects for location recommendation on location-based social networks

By Huiji Gao, Jiliang Tang, Xia Hu, Huan Liu. ACM Conference on Recommender Systems, 2013

509 citations recorded by the source corpus.

Subfields: Information Systems, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/536bd7e8dc5a45ce9300e2f3bf88ab91e2d6ee19

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. Xia Hu
5. Huan Liu
6. Jiliang Tang
7. Jie Bao
8. Mohamed F. Mokbel

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. Guangzhong Sun

In Signal Processing

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

Citations in the corpus

• STAN: Spatio-Temporal Attention Network for Next Location Recommendation (2021, background): “Meanwhile, such a system can also provide users with the convenience to decide where to go and how to plan the day, based on previous visits as well as current status [5, 7, 9, 23, 41].”
• Learning Graph-based POI Embedding for Location-based Recommendation (2016, background): “As suggested in [7, 28], users’ mobility behaviors in the physical world exhibit strong temporal cyclic patterns, and the daily pattern (hours of the day) is one of the most fundamental patterns.”
• GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation (2014, methodology): “, Recall@k and Precision@k, in the top k POI recommendation [24, 12, 5].”
• Recommendations in location-based social networks: a survey (2015, methodology): “Foursquare 3 [33] Check-ins, Friendships, User Three sets of check-ins from 33,596 users”
• GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations (2015, methodology): “, [3, 5, 6, 10, 11, 14, 25, 26, 36]) apply the traditional memory-based or model-based collaborative filtering techniques with the user-POI check-in matrix to compute the preference score between a user and an unvisited POI.”
• Content-Aware Point of Interest Recommendation on Location-Based Social Networks (2015, methodology): “Gao et al. (Gao et al. 2013) investigated the temporal cyclic patterns of check-ins in terms of temporal non-uniformness and temporal consecutiveness.”
• Locative media and data-driven computing experiments (2016, background): “For research concerned with the prediction of preferences in shops or events (e.g. Daly and Geyer, 2011; Gao et al., 2013), it becomes important to find ways to utilise what was known in the existing locative data to infer what was unknown or yet to be known in future events by considering the…”
• Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation (2015, methodology): “• LRT: This is a recently developed matrix factorization method for POI recommendation with time information [7].”