LARS: A Location-Aware Recommender System

By Justin J. Levandoski, Mohamed Sarwat, Ahmed Eldawy, Mohamed F. Mokbel. IEEE International Conference on Data Engineering, 2012

437 citations recorded by the source corpus.

Subfields: Information Systems, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1fd9b0c2e8b8e442a736fc380b543c7f98125c78

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. Charu C. Aggarwal
2. Jia-Dong Zhang
3. Chi-Yin Chow
4. Yu Zheng
5. Jie Bao
6. Mohamed F. Mokbel
7. Hongzhi Yin
8. David Wilkie

In Transportation

1. Yu Zheng
2. Charu C. Aggarwal
3. Jia-Dong Zhang
4. Chi-Yin Chow
5. Jie Bao
6. David Wilkie
7. Mohamed F. Mokbel
8. Gao Cong

In Signal Processing

1. Charu C. Aggarwal
2. Yu Zheng
3. Jie Bao
4. Chi-Yin Chow
5. David Wilkie
6. Jia-Dong Zhang
7. Mohamed F. Mokbel
8. Gao Cong

Citations in the corpus

• Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation (2015, methodology): “extend item-based CF method by considering the travel distance as penalty [9].”
• Exploiting Geographical Neighborhood Characteristics for Location Recommendation (2014, methodology): “Then, a matrix factorization model was fused with geographical and social influence for location recommendation in LBSNs. Levandoski et al. [16] proposed a location-aware recommender system, namely LARS, which employed the location-based ratings to provide recommendation.”
• Recommendations in location-based social networks: a survey (2015, background): “For instance, users in Foursquare visit restaurants close to their homes more frequently than others [55].”
• LCARS: a location-content-aware recommender system (2013, background): “To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.”
• Recommender Systems (2016, positive): “A significant amount of recent work has focussed on location-aware recommender systems [64, 108, 343, 447, 464, 645, 649].”
• GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations (2015, methodology): “Most existing POI recommendation methods (e.g., [3, 5, 6, 10, 11, 14, 25, 26, 37]) 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.”