LCARS: a location-content-aware recommender system

By Hongzhi Yin, Yizhou Sun, Bin Cui, Zhiting Hu, Ling Chen. Knowledge Discovery and Data Mining, 2013

377 citations recorded by the source corpus.

Subfields: Information Systems, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/584485cd290b2bbac397281f35f468520e55a94b

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. Jia-Dong Zhang
2. Chi-Yin Chow
3. Yu Zheng
4. Charu C. Aggarwal
5. Huan Liu
6. Chuan Shi
7. Xing Xie
8. Xia Hu

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. Charu C. Aggarwal

In Signal Processing

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

Citations in the corpus

• Content-Aware Point of Interest Recommendation on Location-Based Social Networks (2015, background): “Yin et al. (Yin et al. 2013) investigated both personal interest and local preference on LBSNs and EBSNs.”
• Learning Graph-based POI Embedding for Location-based Recommendation (2016, methodology): “To evaluate the recommendation method-s, we adopt the evaluation methodology and measurement Accuracy@ k proposed in [17, 25, 27, 26].”
• Recommender Systems (2016, positive): “A significant amount of recent work has focussed on location-aware recommender systems [64, 108, 343, 447, 464, 645, 649].”
• Recommendations in location-based social networks: a survey (2015, background): “2013 [114] further extend the problem by proposing an LCA-LDA model, a location-content-aware probabilistic generative model to quantify both the local preference and the item content information in the recommendation process.”
• GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation (2014, methodology): “For example, in [13, 25, 22], they tried to leverage content information of locations via topic modeling to assist POI recommendation; In [5], Gao et al. proposed distinguishing a user’s latent factors at different times and exploiting several strategies to aggregate a user’s time-dependent latent…”
• Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation (2015, background): “Other types of contexts utilized in POI recommendation include category of POI [13], text description of POI [23, 12, 10, 8, 26], and currently-visited POI [4, 6].”
• Heterogeneous Information Network Embedding for Recommendation (2017, background): “Since CF methods usually suffer from cold-start problem, many works [8], [17], [18] attempt to leverage additional information to improve recommendation performance.”
• GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations (2015, methodology): “• LCARS : This method builds a Location-Content-Aware Recommender System based on the well-known topic model (i.e., latent Dirichlet allocation) to infer personal interest and local preference [25, 26].”
• Exploiting Geographical Neighborhood Characteristics for Location Recommendation (2014, background): “[28] introduced a location-content-aware recommender system that offered a user a set of venues or events, considering both personal interest and the local preference of each individual city.”