N-Most Interesting Location-based Recommender System

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Sumet Darapisut
Komate Amphawan
Sunisa Rimcharoen
Nutthanon Leelathakul


The popular and ubiquitous location-based social networks (LBSNs) ap-peal many users for sharing interesting locations with other users. As the collected data (such as users' profiles, location comment, and suggestion) become a lot larger in size, location-based recommender systems require more effective fillters to be able to suggest potentially preferable locations to users. Location recommendation is more difficult and challenging especially if users have few or no check-in histories as a new user. Therefore, instead of depending on users' check-in histories, previous works focused on creating recommended location lists by leveraging the information given by other users who check in locations in each area. However, previous studies took into account only the frequencies of users for creating recommendation lists and have su˙ered from the cold-start problem where new users have few or none histories. As a result, the recommenders hardly suggest any locations matching the users' preference. In this paper, we propose an enhanced location-recommendation approach called N-most interesting location-based recommender system (NILR) to recommend interesting lo-cations for new users. Our approach can be divided into three phases. First, the NILR discovers interesting locations by taking into account both the visiting frequencies and the preferences of users already in the system. Second, a ranking procedure is executed to create a final recommendation list based on two interestingness scores: one obtained from the HITS-based model (as adopted by [1] and [2]) and the other from our proposed method. Finally, we re-filter interesting locations based on the current location of the new user. Experimental results reveal the NIRL can reach better precision, recall, average ranking and NDGC than HITS by 6%, 6%, 30%and 8% for Tokyo and 24%, 30%, 43% and 15% for New York dataset, respectively.

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How to Cite
S. Darapisut, K. Amphawan, S. Rimcharoen, and N. Leelathakul, “N-Most Interesting Location-based Recommender System”, ECTI-CIT Transactions, vol. 16, no. 1, pp. 84–99, Mar. 2022.
Research Article


Jie Bao, Yu Zheng, and Mohamed F Mokbel. Location-based and preference-aware recommendation using sparse geo-social networking data. In Proceedings of the 20th international conference on advances in geographic information systems, pages 199-208, 2012.

Hakan Bagci and Pinar Karagoz. Context-aware location recommendation by using a random walk-based approach. Knowledge and Information Systems, 47(2):241-260, 2016.

Yu Zheng and Xiaofang Zhou. Location-based social networks: Users. pages 243-276, 2011.

Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. Urban computing: Concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol., 5(3), Sep 2014.

Yonghong Yu and Xingguo Chen. A survey of point-of-interest recommendation in locationbased social networks. In Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.

Shuai Xu, Xiaoming Fu, Jiuxin Cao, Bo Liu, and Zhixiao Wang. Survey on user location prediction based on geo-social networking data. World Wide Web, pages 1-44, 2020.

Dequan Zhou, Bin Wang, Seyyed Mohammadreza Rahimi, and Xin Wang. A study of recommending locations on location-based social network by collaborative filtering. In Leila Kosseim and Diana Inkpen, editors, Advances in Artificial Intelligence, pages 255-266, Berlin, Heidelberg, 2012. Springer Berlin Heidelberg.

J. J. Levandoski, M. Sarwat, A. Eldawy, and M. F. Mokbel. Lars: A location-aware recommender system. In 2012 IEEE 28th International Conference on Data Engineering, pages 450-461, 2012.

Zhijun Ding, Xiaolun Li, Changjun Jiang, and Mengchu Zhou. Objectives and state-of-the-art of location-based social network recommender systems. ACM Comput. Surv., 51(1), January 2018.

Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. Mining interesting locations and travel sequences from gps trajectories. In Proceedings of the 18th international conference on World wide web, pages 791-800, 2009.

Sumet Darapisut, Komate Amphawan, Sunisa Rimcharoen, and Nutthanon Leelathakul. Nilr: N-most interesting location-based recommender system. In Conference on Smart Media and Applications, 2020.

Dingqi Yang, Daqing Zhang, Vincent W Zheng, and Zhiyong Yu. Modeling user activity

preference by leveraging user spatial temporal characteristics in lbsns. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(1):129-142, 2014.

Yuankai Ying, Ling Chen, and Gencai Chen. A temporal-aware poi recommendation system

using context-aware tensor decomposition and weighted hits. Neurocomputing, 242:195-205, 2017.

Robin Burke. Hybrid Web Recommender Systems, pages 377-408. Springer Berlin Heidelberg,

Berlin, Heidelberg, 2007.

Claudio Biancalana, Fabio Gasparetti, Alessandro Micarelli, and Giuseppe Sansonetti. An approach to social recommendation for contextaware mobile services. ACM Trans. Intell. Syst.

Technol., 4(1), February 2013.

Shenglin Zhao, Irwin King, and Michael R LyuA survey of point-of-interest recommendation in location-based social networks. arXiv preprint arXiv:1607.00647, 2016.

Xiangye Xiao, Yu Zheng, Qiong Luo, and Xing Xie. Finding similar users using category-based

location history. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS’10, pages 442-445, New York, NY, USA, 2010. Association for Computing Machinery.

Panagiotis Symeonidis, Alexis Papadimitriou, Yannis Manolopoulos, Pinar Senkul, and Ismail Toroslu. Geo-social recommendations based on incremental tensor reduction and local path traversal. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN ’11, pages 89-96, New York, NY, USA, 2011. Association for Computing Machinery.

Henan Wang, Guoliang Li, and Jianhua Feng. Group-based personalized location recommendation on social networks. In Lei Chen, Yan Jia, Timos Sellis, and Guanfeng Liu, editors, Web Technologies and Applications, pages 68-80, Cham, 2014. Springer International Publishing.

Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat Thalmann. Timeaware point-of-interest recommendation. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’13, pages 363-372, New York, NY, USA, 2013. Association for Computing Machinery.

Kwan Hui Lim, Jeffrey Chan, Christopher Leckie, and Shanika Karunasekera. Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency. 54(2):375-406, February 2018.

Kwan Hui Lim, Jeffrey Chan, Shanika Karunasekera, and Christopher Leckie. Tour recommendation and trip planning using location-based social media: a survey. Knowledge and Information Systems, pages 1-29, 2019.

Vincent W. Zheng, Bin Cao, Yu Zheng, Xing Xie, and Qiang Yang. Collaborative filtering meets mobile recommendation: A usercentered approach. In Proceedings of the TwentyFourth AAAI Conference on Artificial Intelligence, AAAI’10, pages 236-241. AAAI Press, 2010.

Vincent W. Zheng, Yu Zheng, Xing Xie, and Qiang Yang. Collaborative location and activity recommendations with gps history data. In Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pages 1029-1038, New York, NY, USA, 2010. Association for Computing Machinery.

Xin Liu, Yong Liu, Karl Aberer, and Chunyan Miao. Personalized point-of-interest recommendation by mining users’ preference transition. In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, CIKM ’13, pages 733-738, New York, NY, USA, 2013.

Daniel Preot¸iuc-Pietro and Trevor Cohn. Mining user behaviours: a study of check-in patterns in location based social networks. In Proceedings of the 5th annual ACM web science conference, pages 306-315, 2013.

Yali Si, Fuzhi Zhang, and Wenyuan Liu. Ctfara: An adaptive method for poi recommendation based on check-in and temporal features. Knowledge-Based Systems, 128:59-70, 2017.

Jiuxin Cao, Shuai Xu, Xuelin Zhu, Renjun Lv, and Bo Liu. Effective fine-grained location prediction based on user check-in pattern in

lbsns. Journal of Network and Computer Applications, 108:64-75, 2018.

Ramesh Baral and Tao Li. Exploiting the roles of aspects in personalized poi recommender systems. Data Mining and Knowledge Discovery, 32(2):320-343, 2018.

Mingxin Gan and Ling Gao. Discovering memory-based preferences for poi recommendation in location-based social networks.

ISPRS International Journal of Geo-Information, 8(6):279, 2019.

Mingjun Xin, Lijun Wu, and Shunxian Li. A user profile awareness service collaborative recommendation algorithm under lbsn environment. International Journal of Cooperative Information Systems, 28(03):1950008, 2019.

Jon M Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM

(JACM), 46(5):604-632, 1999.

X. Long and J. Joshi. A hits-based poi recommendation algorithm for location-based social networks. In 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), pages 642-647, 2013.

Jialiang Chen, Xin Li, William K. Cheung, and Kan Li. Effective successive poi recommendation inferred with individual behavior and group preference. Neurocomput., 210:174-184, oct 2016.

Diyah Puspitaningrum, Julio Fernando, Edo Afriando, Ferzha Putra Utama, Rina Rahmadini, and Y Pinata. Finding local experts for dynamic recommendations using lazy random walk. In 2019 7th International Conference on Cyber and IT Service Management (CITSM), volume 7, pages 1-6. IEEE, 2019.