ABSTRACT
With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of- Interest (POI) prediction has been extensively studied. However, much of the research has been conducted solely on voluntary check-in datasets collected from social apps such as Foursquare or Yelp. While these data contain rich information about recreational activities (e.g., restaurants, nightlife, and entertainment), information about more prosaic aspects of people's lives is sparse. is not only limits our understanding of users' daily routines, but more importantly the modeling assumptions developed based on characteristics of recreation-based data may not be suitable for richer check-in data. In this work, we present an analysis of education "check-in" data using WiFi access logs collected at Purdue University. We propose a heterogeneous graph-based method to encode the correlations between users, POIs, and activities, and then jointly learn embeddings for the vertices. We evaluate our method compared to previous state-of-the-art POI prediction methods, and show that the assumptions made by previous methods signicantly degrade performance on our data with dense(r) activity signals. We also show how our learned embeddings could be used to identify similar students (e.g., for friend suggestions).
- Long Chen, Fajie Yuan, Joemon M Jose, and Weinan Zhang. 2018. Improving Negative Sampling for Word Representation using Self-embedded Features Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 99--107. Google ScholarDigital Library
- Ting Chen and Yizhou Sun. 2017. Task-guided and path-augmented heterogeneous network embedding for author identification. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM, 295--304. Google ScholarDigital Library
- Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 135--144. Google ScholarDigital Library
- Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. 2015. Personalized Ranking Metric Embedding for Next New POI Recommendation. IJCAI. 2069--2075. Google ScholarDigital Library
- Jing He, Xin Li, Lejian Liao, Dandan Song, and William K Cheung. 2016. Inferring a Personalized Next Point-of-Interest Recommendation Model with Latent Behavior Patterns.. In AAAI. 137--143. Google ScholarDigital Library
- S Kylasa, G Kollias, and A Grama. 2016. Social ties and checkin sites: connections and latent structures in location-based social networks. Social Network Analysis and Mining Vol. 6, 1 (2016).Google Scholar
- Aaron Q Li, Amr Ahmed, Sujith Ravi, and Alexander J Smola. 2014. Reducing the sampling complexity of topic models. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 891--900. Google ScholarDigital Library
- L Maaten and G Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research Vol. 9, Nov (2008).Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119. Google ScholarDigital Library
- Anastasios Noulas, Salvatore Scellato, Cecilia Mascolo, and Massimiliano Pontil. 2011. An empirical study of geographic user activity patterns in foursquare. ICwSM Vol. 11, 70--573 (2011), 2.Google Scholar
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 701--710. Google ScholarDigital Library
- Benjamin Recht, Christopher Re, Stephen Wright, and Feng Niu. 2011. Hogwild: A lock-free approach to parallelizing stochastic gradient descent Advances in neural information processing systems. 693--701. Google ScholarDigital Library
- Yizhou Sun, Hongzhi Yin, and Xiang Ren. 2017. Recommendation in context-rich environment: An information network analysis approach. In Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, 941--945. Google ScholarDigital Library
- Jian Tang, Meng Qu, and Qiaozhu Mei. 2015. Pte: Predictive text embedding through large-scale heterogeneous text networks Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1165--1174. Google ScholarDigital Library
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1067--1077. Google ScholarDigital Library
- Weiqing Wang, Hongzhi Yin, Ling Chen, Yizhou Sun, Shazia Sadiq, and Xiaofang Zhou. 2015. Geo-SAGE: A geographical sparse additive generative model for spatial item recommendation. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1255--1264. Google ScholarDigital Library
- Weiqing Wang, Hongzhi Yin, Shazia Sadiq, Ling Chen, Min Xie, and Xiaofang Zhou. 2016. Spore: A sequential personalized spatial item recommender system Data Engineering (ICDE), 2016 IEEE 32nd International Conference on. IEEE, 954--965.Google Scholar
- Min Xie, Hongzhi Yin, Hao Wang, Fanjiang Xu, Weitong Chen, and Sen Wang. 2016. Learning graph-based poi embedding for location-based recommendation Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 15--24. Google ScholarDigital Library
- Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan, and Jiawei Han. 2017. Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1245--1254. Google ScholarDigital Library
- Zijun Yao. 2018. Exploiting Human Mobility Patterns for Point-of-Interest Recommendation Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 757--758. Google ScholarDigital Library
- H Yin, W Wang, H Wang, L Chen, and X Zhou. 2017. Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation. IEEE Transactions on Knowledge and Data Engineering Vol. 29, 11 (2017).Google ScholarDigital Library
- Chao Zhang, Keyang Zhang, Quan Yuan, Fangbo Tao, Luming Zhang, Tim Hanratty, and Jiawei Han. 2017. ReAct: Online Multimodal Embedding for Recency-Aware Spatiotemporal Activity Modeling. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 245--254. Google ScholarDigital Library
- Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories Proceedings of the 18th international conference on World wide web. ACM, 791--800. Google ScholarDigital Library
Index Terms
- Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
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