Abstract
With the development of mobile Internet, many location-based services have accumulated a large amount of data that can be used for point-of-interest (POI) recommendation. However, there are still challenges in developing an unified framework to incorporate multiple factors associated with both POIs and users due to the heterogeneity and implicity of this information. To alleviate the problem, this work proposes a novel group-based method for POI recommendation jointly considering the reviews, categories, and geographical locations, called the Group-based Temporal Sentiment-Aspect-Region Recurrent Neural Network (GTSAR-RNN). We divide the users into different groups and then train an individual RNN for each group with the goal of improving its pertinence. In GTSAR-RNN, we consider not only the effects of temporal and geographical contexts but also the users’ sentimental opinions on locations. Experimental results show that GTSAR-RNN acquires significant improvements over the baseline methods on real datasets.
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473.Google Scholar
- David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3 (2003), 993--1022. http://www.jmlr.org/papers/v3/blei03a.html.Google Scholar
Digital Library
- John S. Breese, David Heckerman, and Carl Myers Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI’98). 43--52. https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=18smnu=28article_id=2318proceeding_id=14.Google Scholar
Digital Library
- Xin Cao, Gao Cong, and Christian S. Jensen. 2010. Mining significant semantic locations from GPS data. Proceedings of the VLDB Endowment 3, 1 (2010), 1009--1020. DOI:https://doi.org/10.14778/1920841.1920968Google Scholar
Digital Library
- Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. 2016. A unified point-of-interest recommendation framework in location-based social networks. ACM Transactions on Intelligent Systems and Technology 8, 1 (2016), Article 10, 21 pages. DOI:https://doi.org/10.1145/2901299Google Scholar
Digital Library
- Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI’13). 2605--2611. http://www.aaai.org/ocs/index.php/IJCAI/IJCAI13/paper/view/6633.Google Scholar
Digital Library
- Jian Cheng, Ting Yuan, Jinqiao Wang, and Hanqing Lu. 2014. Group latent factor model for recommendation with multiple user behaviors. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’14). 995--998. DOI:https://doi.org/10.1145/2600428.2609493Google Scholar
Digital Library
- Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for YouTube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. 191--198. DOI:https://doi.org/10.1145/2959100.2959190Google Scholar
Digital Library
- G. E. Hinton, D. E. Rumelhart, and R. J. Williams. 1986. Learning representations by back-propagating errors. Nature 323 (1986), 533--536.Google Scholar
Cross Ref
- Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. 2015. Personalized ranking metric embedding for next new POI recommendation. In Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI’15). 2069--2075. http://ijcai.org/Abstract/15/293.Google Scholar
Digital Library
- Lei Guo, Jun Ma, Zhumin Chen, and Huan Zhong. 2015. Learning to recommend with social contextual information from implicit feedback. Soft Computing 19, 5 (2015), 1351--1362. DOI:https://doi.org/10.1007/s00500-014-1347-0Google Scholar
Digital Library
- Yohan Jo and Alice H. Oh. 2011. Aspect and sentiment unification model for online review analysis. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM’11). 815--824. DOI:https://doi.org/10.1145/1935826.1935932Google Scholar
- Junghoon Lee, In-Hye Shin, and Gyung-Leen Park. 2008. Analysis of the passenger pick-up pattern for taxi location recommendation. In Proceedings of the 2008 4th International Conference on Networked Computing and Advanced Information Management. 199--204. DOI:https://doi.org/10.1109/NCM.2008.24Google Scholar
- Xutao Li, Gao Cong, Xiaoli Li, Tuan-Anh Nguyen Pham, and Shonali Krishnaswamy. 2015. Rank-GeoFM: A ranking based geographical factorization method for point of interest recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’15). 433--442. https://doi.org/10.1145/2766462.2767722Google Scholar
Digital Library
- Bing Liu. 2012. Sentiment Analysis and Opinion Mining. Morgan 8 Claypool. DOI:https://doi.org/10.2200/S00416ED1V01Y201204HLT016Google Scholar
- Kuan Liu, Xing Shi, Anoop Kumar, Linhong Zhu, and Prem Natarajan. 2016. Temporal learning and sequence modeling for a job recommender system. In Proceedings of the Recommender Systems Challenge (RecSys Challenge’16). Article 7, 4 pages. DOI:https://doi.org/10.1145/2987538.2987540Google Scholar
- Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the next location: A recurrent model with spatial and temporal contexts. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI’16). 194--200. http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11900.Google Scholar
Digital Library
- Yong Liu, Wei Wei, Aixin Sun, and Chunyan Miao. 2014. Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM’14). 739--748. DOI:https://doi.org/10.1145/2661829.2662002Google Scholar
Digital Library
- Yue Lu, Malú Castellanos, Umeshwar Dayal, and ChengXiang Zhai. 2011. Automatic construction of a context-aware sentiment lexicon: An optimization approach. In Proceedings of the 20th International Conference on World Wide Web (WWW’11). 347--356. DOI:https://doi.org/10.1145/1963405.1963456Google Scholar
- Anastasios Noulas, Salvatore Scellato, Neal Lathia, and Cecilia Mascolo. 2012. A random walk around the city: New venue recommendation in location-based social networks. In Proceedings of the 2012 International Conference on Privacy, Security, Risk, and Trust and the 2012 International Conference on Social Computing. 144--153. DOI:https://doi.org/10.1109/SocialCom-PASSAT.2012.70Google Scholar
- Guang Qiu, Bing Liu, Jiajun Bu, and Chun Chen. 2011. Opinion word expansion and target extraction through double propagation. Computational Linguistics 37, 1 (2011), 9--27. DOI:https://doi.org/10.1162/coli_a_00034Google Scholar
Digital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI’09). 452--461. https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=18smnu=28article_id=16308proceeding_id=25.Google Scholar
Digital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web (WWW’10). 811--820. DOI:https://doi.org/10.1145/1772690.1772773Google Scholar
Digital Library
- Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic matrix factorization. In Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS’07). 1257--1264. http://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.Google Scholar
Digital Library
- Yang Song, Ali Mamdouh Elkahky, and Xiaodong He. 2016. Multi-rate deep learning for temporal recommendation. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’16). 909--912. DOI:https://doi.org/10.1145/2911451.2914726Google Scholar
Digital Library
- Hao Wang, Yanmei Fu, Qinyong Wang, Hongzhi Yin, Changying Du, and Hui Xiong. 2017. A location-sentiment-aware recommender system for both home-town and out-of-town users. In Proceedings of the23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’17). 1135--1143. DOI:https://doi.org/10.1145/3097983.3098122Google Scholar
- 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 (KDD’17). 1245--1254. DOI:https://doi.org/10.1145/3097983.3098094Google Scholar
Digital Library
- Cheng Yang, Maosong Sun, Wayne Xin Zhao, Zhiyuan Liu, and Edward Y. Chang. 2017. A neural network approach to jointly modeling social networks and mobile trajectories. ACM Transactions on Information Systems 35, 4 (2017), Article 36, 28 pages. DOI:https://doi.org/10.1145/3041658Google Scholar
Digital Library
- Dingqi Yang, Daqing Zhang, Zhiyong Yu, and Zhu Wang. 2013. A sentiment-enhanced personalized location recommendation system. In Proceedings of the 24th ACM Conference on Hypertext and Social Media (HT’13). 119--128. DOI:https://doi.org/10.1145/2481492.2481505Google Scholar
Digital Library
- Mao Ye, Peifeng Yin, and Wang-Chien Lee. 2010. Location recommendation for location-based social networks. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS’10). 458--461. DOI:https://doi.org/10.1145/1869790.1869861Google Scholar
Digital Library
- Hongzhi Yin, Bin Cui, Xiaofang Zhou, Weiqing Wang, Zi Huang, and Shazia W. Sadiq. 2016. Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Transactions on Information Systems 35, 2 (2016), Article 11, 44 pages. DOI:https://doi.org/10.1145/2873055Google Scholar
Digital Library
- Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat-Thalmann. 2013. Time-aware point-of-interest recommendation. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’13). 363--372. DOI:https://doi.org/10.1145/2484028.2484030Google Scholar
Digital Library
- Quan Yuan, Gao Cong, and Aixin Sun. 2014. Graph-based point-of-interest recommendation with geographical and temporal influences. In Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM’14). 659--668. DOI:https://doi.org/10.1145/2661829.2661983Google Scholar
Digital Library
- Wei Zhang and Jianyong Wang. 2015. Location and time aware social collaborative retrieval for new successive point-of-interest recommendation. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM’15). 1221--1230. DOI:https://doi.org/10.1145/2806416.2806564Google Scholar
Digital Library
- Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, and Tie-Yan Liu. 2014. Sequential click prediction for sponsored search with recurrent neural networks. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI’14). 1369--1375. http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8529.Google Scholar
- Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’14). 83--92. DOI:https://doi.org/10.1145/2600428.2609579Google Scholar
Digital Library
- Zhiqian Zhang, Chenliang Li, Zhiyong Wu, Aixin Sun, Dengpan Ye, and Xiangyang Luo. 2017. NEXT: A neural network framework for next POI recommendation. arXiv:1704.04576.Google Scholar
- Kaiqi Zhao, Gao Cong, Quan Yuan, and Kenny Q. Zhu. 2015. SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews. In Proceedings of the 2015 IEEE 31st International Conference on Data Engineering. 675--686. DOI:https://doi.org/10.1109/ICDE.2015.7113324Google Scholar
- Zhe Zhao, Tao Liu, Shen Li, Bofang Li, and Xiaoyong Du. 2017. Guiding the training of distributed text representation with supervised weighting scheme for sentiment analysis. Data Science and Engineering 2, 2 (2017), 178--186.Google Scholar
- Bolong Zheng, Han Su, Wen Hua, Kai Zheng, Xiaofang Zhou, and Guohui Li. 2017. Efficient clue-based route search on road networks. IEEE Transactions on Knowledge and Data Engineering 29, 9 (2017), 1846--1859.Google Scholar
Digital Library
- Bolong Zheng, Han Su, Kai Zheng, and Xiaofang Zhou. 2016. Landmark-based route recommendation with crowd intelligence. Data Science and Engineering 1, 2 (2016), 86--100.Google Scholar
Cross Ref
- Kaiping Zheng, Wei Wang, Jinyang Gao, Kee Yuan Ngiam, Beng Chin Ooi, and James Wei Luen Yip. 2017. Capturing feature-level irregularity in disease progression modeling. In Proceedings of the 26th International Conference on Information and Knowledge Management (CIKM’17). 1579--1588. DOI:https://doi.org/10.1145/3132847.3132944Google Scholar
- Kai Zheng, Yu Zheng, Nicholas Jing Yuan, Shuo Shang, and Xiaofang Zhou. 2014. Online discovery of gathering patterns over trajectories. IEEE Transactions on Knowledge and Data Engineering 26, 8 (2014), 1974--1988. DOI:https://doi.org/10.1109/TKDE.2013.160Google Scholar
Cross Ref
- Nan Zheng and Qiudan Li. 2011. A recommender system based on tag and time information for social tagging systems. Expert Systems with Applications 38, 4 (2011), 4575--4587. DOI:https://doi.org/10.1016/j.eswa.2010.09.131Google Scholar
Digital Library
- Yu Zheng. 2015. Trajectory data mining: An overview. ACM Transactions on Intelligent Systems and Technology 6, 3 (2015), Article 29, 41 pages. DOI:https://doi.org/10.1145/2743025Google Scholar
Digital Library
Index Terms
Group-Based Recurrent Neural Networks for POI Recommendation
Recommendations
Spatiotemporal Representation Learning for Translation-Based POI Recommendation
The increasing proliferation of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Time and location are the two most important contextual factors in the ...
Personalized POI recommendation based on check-in data and geographical-regional influence
ICMLSC '19: Proceedings of the 3rd International Conference on Machine Learning and Soft ComputingNowadays, many people like to share the places they visited to their friends in the location-based social networks (LBSNs). Therefore, LBSNs have accumulated large-scale user check-in data and the availability of these data enables many location-based ...
Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks
AbstractIn mobile social networks, next point-of-interest (POI) recommendation is a very important function that can provide personalized location-based services for mobile users. In this paper, we propose a recurrent neural network (RNN)-based next POI ...






Comments