SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews

By Gao Cong, Quan Yuan, Kenny Q. Zhu. 2015

77 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Transportation

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. Xu Chen
2. Yongfeng Zhang
3. Gao Cong
4. S. S. Iyengar
5. Xiaolong Zhu
6. Tao Li
7. Ramesh Baral
8. Carl Yang

In Sociology and Political Science

1. Yongfeng Zhang
2. Xu Chen
3. Gao Cong
4. Quan Yuan
5. Jiawei Han

In Transportation

1. Yongfeng Zhang
2. Xu Chen
3. Quan Yuan
4. Gao Cong
5. Jiawei Han
6. Xutao Li

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “Zhao et al. [2014] represented products and users in the same demographic feature space, and used the weights of the demographic features learned by a ranking function to intuitively explain the results; Zhao et al.”
• 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].”
• Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (2017, unknown): “Furthermore, [4] proposed a Sentiment Utility Logistic Model to recommend not only items but also item aspects to provide beŠer experience for the users, and [43] jointly considered sentiment, aspect, and regional features based on geographical topic modeling for location-aware recommendation.”
• ReEL: Review Aware Explanation of Location Recommendation (2018, result): “Contrary to that, some of the studies [17, 18, 35, 36, 40, 41, 43, 57] have already claimed the user persuasiveness due to explainability in real-world systems.”
• 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.”