ABSTRACT
Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation. However, previous studies fail to capture crucial information about POIs such as categorical information.
In this paper, we propose a novel neural model that generates a POI embedding incorporating sequential and categorical information from POIs. Our model consists of a check-in module and a category module. The check-in module captures the geographical influence of POIs derived from the sequence of users' check-ins, while the category module captures the characteristics of POIs derived from the category information. To validate the efficacy of the model, we experimented with two large-scale LBSN datasets. Our experimental results demonstrate that our approach significantly outperforms state-of-the-art POI recommendation methods.
- Mohammad Aliannejadi and Fabio Crestani. 2018. Personalized Context-Aware Point of Interest Recommendation. ACM Trans. Inf. Syst. , Vol. 36, 4 (2018), 45:1--45:28.Google ScholarDigital Library
- Mohammad Aliannejadi, Dimitrios Rafailidis, and Fabio Crestani. 2018. A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion. In ICTIR. ACM , 19--26.Google Scholar
- Buru Chang, Yonggyu Park, Donghyeon Park, Seongsoon Kim, and Jaewoo Kang. 2018. Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation.. In IJCAI. 3301--3307.Google Scholar
- Chen Cheng, Haiqin Yang, Irwin King, and Michael R Lyu. 2012. Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks.. In AAAI, Vol. 12. 17--23.Google ScholarDigital Library
- Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio. 2010. Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research , Vol. 11, Feb (2010), 625--660.Google ScholarDigital Library
- Shanshan Feng, Gao Cong, Bo An, and Yeow Meng Chee. 2017. POI2Vec: Geographical Latent Representation for Predicting Future Visitors.. In AAAI . 102--108.Google Scholar
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 173--182.Google ScholarDigital Library
- Xutao Li, Gao Cong, Xiao-Li Li, Tuan-Anh Nguyen Pham, and Shonali Krishnaswamy. 2015. Rank-geofm: A ranking based geographical factorization method for point of interest recommendation. In SIGIR. 433--442.Google Scholar
- Xin Liu, Yong Liu, and Xiaoli Li. 2016. Exploring the Context of Locations for Personalized Location Recommendations.. In IJCAI . 1188--1194.Google Scholar
- Yiding Liu, Tuan-Anh Nguyen Pham, Gao Cong, and Quan Yuan. 2017. An experimental evaluation of point-of-interest recommendation in location-based social networks. VLDB , Vol. 10 (2017), 1010--1021.Google ScholarDigital Library
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).Google Scholar
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI. 452--461.Google ScholarDigital Library
- Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In SIGIR . 325--334.Google Scholar
- 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. ACM, 363--372.Google ScholarDigital Library
- Jia-Dong Zhang and Chi-Yin Chow. 2015. GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations. In SIGIR. 443--452.Google Scholar
- Shuai Zhang, Lina Yao, Yi Tay, Xiwei Xu, Xiang Zhang, and Liming Zhu. 2018. Metric Factorization: Recommendation beyond Matrix Factorization. arXiv preprint arXiv:1802.04606 (2018).Google Scholar
- Pengpeng Zhao, Xiefeng Xu, Yanchi Liu, Ziting Zhou, Kai Zheng, Victor S Sheng, and Hui Xiong. 2017a. Exploiting hierarchical structures for POI recommendation. In 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 655--664.Google ScholarCross Ref
- Shenglin Zhao, Tong Zhao, Irwin King, and Michael R Lyu. 2017b. Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation. In WWW . 153--162.Google ScholarDigital Library
Index Terms
- Category-Aware Location Embedding for Point-of-Interest Recommendation
Recommendations
Time-aware point-of-interest recommendation
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrievalThe availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to ...
Learning geographical preferences for point-of-interest recommendation
KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data miningThe problem of point of interest (POI) recommendation is to provide personalized recommendations of places of interests, such as restaurants, for mobile users. Due to its complexity and its connection to location based social networks (LBSNs), the ...
Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge ManagementThe availability of user check-in data in large volume from the rapid growing location-based social networks (LBSNs) enables a number of important location-aware services. Point-of-interest (POI) recommendation is one of such services, which is to ...
Comments