Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation

By Xutao Li, Gao Cong, Xiaoli Li, Tuan-Anh Nguyen Pham, Shonali Priyadarsini Krishnaswamy. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015

359 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/815cabc10a0b9a3dee71954e2bd1c9cae3f22c4d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Liang Wang
2. Tat-seng Chua
3. Dacheng Tao
4. Xiang Wang
5. Tien-Ping Tan
6. Liqiang Nie
7. Luc Van Gool
8. Yi Yang

In Information Systems

1. Xiang Wang
2. Xiangnan He
3. Tat-seng Chua
4. Liqiang Nie
5. Qiang Liu
6. Gao Cong
7. Xuemeng Song
8. Liang Wang

In Transportation

1. Quan Yuan
2. Liang Wang
3. Tien-Ping Tan
4. Tat-seng Chua
5. Gao Cong
6. Jiawei Han
7. Xiang Wang
8. Xutao Li

Citations in the corpus

• Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (2017, background): “While traditional recommender systems mostly work with ratings and reviews [20], POI recommendations naturally come with various context, such as geographical information [24, 25, 28, 47, 49], categorical information [53, 55], social information [22, 38, 50, 52], temporal information [48, 56], etc.”
• Practical Privacy Preserving POI Recommendation (2020, background): “CF, factorization-based models achieve promising performance [8, 30, 33], which aim to learn user and item latent factors based on known user-item action histories such as ratings and clicks.”
• HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction (2018, background): “A lot of works [Li et al. , 2015; Liu et al. , 2016] leverage temporal and spatial features to boost prediction performance.”
• Unifying Virtual and Physical Worlds (2017, methodology): “We thus argue that, as compared to pure online or offline network, appropriate aggregation of them can provide us a better way to comprehensively understand user behaviors and their underlying organizational principles.”
• Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts (2016, background): “Recently, some work considers users’ interest to the neighborhood of the destination location (Liu et al. 2014; Li et al. 2015).”
• Personalized Ranking Metric Embedding for Next New POI Recommendation (2015, background): “Importance of POI recommendation has attracted a significant amount of research interest on developing recommendation techniques [Cho et al., 2011; Ye et al., 2011; Cheng et al., 2012; Yuan et al., 2013; Lian et al., 2014; Li et al., 2015].”