Social media mining and visualization for point-of-interest recommendation

doi:10.1016/S1005-8885(17)60189-4

中国邮电高校学报(英文) ›› 2017, Vol. 24 ›› Issue (1): 67-76.doi: 10.1016/S1005-8885(17)60189-4

• Artificial Intelligence • 上一篇下一篇

Social media mining and visualization for point-of-interest recommendation

Ren Xingyi, Song Meina, E Haihong, Song Junde

1. School of Computing, Beijing University of Posts and Telecommunications
2. Engineering Research Center of Information Networks, Beijing University of Posts and Telecommunications

收稿日期:2016-09-05 修回日期:2016-12-19 出版日期:2017-02-28 发布日期:2017-02-28
通讯作者: 任星怡 E-mail:xyren@bupt.edu.cn
基金资助:
This work was supported by the National Key Project of Scientific and Technical Supporting Programs of China (2014BAH26F02).

Social media mining and visualization for point-of-interest recommendation

Ren Xingyi, Song Meina, E Haihong, Song Junde

1. School of Computing, Beijing University of Posts and Telecommunications
2. Engineering Research Center of Information Networks, Beijing University of Posts and Telecommunications

Received:2016-09-05 Revised:2016-12-19 Online:2017-02-28 Published:2017-02-28
Contact: Xing-Yi REN E-mail:xyren@bupt.edu.cn
Supported by:
This work was supported by the National Key Project of Scientific and Technical Supporting Programs of China (2014BAH26F02).

摘要/Abstract

摘要： With the rapid growth of location-based social networks (LBSNs), point-of-interest (POI) recommendation has become an important research problem. As one of the most representative social media platforms, Twitter provides various real-life information for POI recommendation in real time. Despite that POI recommendation has been actively studied, tweet images have not been well utilized for this research problem. State-of-the-art visual features like convolutional neural network (CNN) features have shown significant performance gains over the traditional bag-of-visual-words in unveiling the image’s semantics. Unfortunately, they have not been employed for POI recommendation from social websites. Hence, how to make the most of tweet images to improve the performance of POI recommendation and visualization remains open. In this paper, we thoroughly study the impact of tweet images on POI recommendation for different POI categories using various visual features. A novel topic model called social media Twitter-latent Dirichlet allocation (SM-TwitterLDA) which jointly models five Twitter features, (i.e., text, image, location, timestamp and hashtag) is designed to discover POIs from the sheer amount of tweets. Moreover, each POI is visualized by representative images selected on three predefined criteria. Extensive experiments have been conducted on a real-life tweet dataset to verify the effectiveness of our method.

关键词: social media, Twitter, POI recommendation, visualization

Abstract: With the rapid growth of location-based social networks (LBSNs), point-of-interest (POI) recommendation has become an important research problem. As one of the most representative social media platforms, Twitter provides various real-life information for POI recommendation in real time. Despite that POI recommendation has been actively studied, tweet images have not been well utilized for this research problem. State-of-the-art visual features like convolutional neural network (CNN) features have shown significant performance gains over the traditional bag-of-visual-words in unveiling the image’s semantics. Unfortunately, they have not been employed for POI recommendation from social websites. Hence, how to make the most of tweet images to improve the performance of POI recommendation and visualization remains open. In this paper, we thoroughly study the impact of tweet images on POI recommendation for different POI categories using various visual features. A novel topic model called social media Twitter-latent Dirichlet allocation (SM-TwitterLDA) which jointly models five Twitter features, (i.e., text, image, location, timestamp and hashtag) is designed to discover POIs from the sheer amount of tweets. Moreover, each POI is visualized by representative images selected on three predefined criteria. Extensive experiments have been conducted on a real-life tweet dataset to verify the effectiveness of our method.

Key words: social media, Twitter, POI recommendation, visualization

中图分类号:

TP301.6

Ren Xingyi, Song Meina, E Haihong, Song Junde. Social media mining and visualization for point-of-interest recommendation[J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2017, 24(1): 67-76.

参考文献

1. Ferrari L, Rosi A, Mamei M, et al. Extracting urban patterns from location-based social networks. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, Nov 1, 2011, Chicago, IL, USA. New York, NY, USA: ACM, 2011: 9-16

2. Yin H Z, Sun Y Z, Cui B, et al. LCARS: a location-content-aware recommender system. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug 11-14, 2013, Chicago, IL, USA. New York, NY, USA: ACM, 2013: 221-229

3. Yuan Q, Cong G, Ma Z Y, et al. Time-aware point-of-interest recommendation. Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 28-Aug 1, 2013, Dublin, Ireland. New York, NY, USA: ACM, 2013: 363-372

4. Zhang J D, Chow C Y. Geosoca: exploiting geographical, social and categorical correlations for point-of-interest recommendations. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Aug 9-13, 2015, Santiago, Chile. New York, NY, USA: ACM, 2015: 443-452

5. Lim K H. Recommending tours and places-of-interest based on user interests from geo-tagged photos. Proceedings of the 2015 ACM SIGMOD on PhD Symposium, May 31-Jun 4, 2015, Melbourne, Australia. New York, NY, USA: ACM, 2015: 33-38

6. Jiang S H, Qian X M, Mei T, et al. Personalized travel sequencer recommendation on multi-source big social media. IEEE Transactions on Big Data, 2016, 2(1): 43-56

7. Yin H Z, Cui B, Huang Z, et al. Joint modeling of user’ interests and mobility patterns for point-of-interest recommendation. Proceedings of the 23rd ACM International Conference on Multimedia, Oct 26-30, 2015, Brisbane, Australia. New York, NY, USA: ACM, 2015: 819-822

8. Wu C C, Mei T, Hsu W H, et al. Learning to personalize trending image search suggestion. Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 6-11, 2014, Gold Coast, Australia. New York, NY, USA: ACM, 2014: 727-736

9. Huang Z, Hu B, Cheng H, et al. Mining near-duplicate graph for cluster-based reranking of web video search results. ACM Transactions on Information Systems, 2010, 28(4): 2349-2370

10. Bian J W, Yang Y, Chua T S. Multimedia summarization for trending topics in microblogs. Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, Oct 27-Nov 1, 2013, San Francisco, CA, USA. New York, NY, USA: ACM, 2013: 1807-1812

11. Seah B S, Bhowmick S S, Sun A X. PRISM: concept-preserving social image search results summarization. Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 6-11, 2014, Gold Coast, Australia. New York, NY, USA: ACM, 2014: 737-746

12. Chen T, Lu D Y, Kan M Y, et al. Understanding and classifying image tweets. Proceedings of the 21st ACM International Conference on Multimedia, Oct 21-25, 2013, Barcelona, Spain. New York, NY, USA: ACM, 2013: 781-784

13. Hsu W H, Kennedy L S, Chang S F. Video search reranking through random walk over document-level context graph. Proceedings of the 15th ACM International Conference on Multimedia, Sept 24-29, 2007, Augsburg, Germany. New York, NY, USA: ACM, 2007: 971-980

14. Mei Q Z, Zhou D Y, Church K. Query suggestion using hitting time. Proceedings of the 17th ACM Conference on Information and Knowledge Management, Oct 26-30, 2008, Napa Valley, CA, USA. New York, NY, USA: ACM, 2008: 469-478

15. Oquab M, Bottou L, Laptev I, et al. Learning and transferring mid-level image representations using convolutional neural networks. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Jun 24-27, 2014, Columbus, OH, USA. Piscataway, NJ, USA: IEEE, 2014: 1717-1724

16. Jiang D, Leung K W T, Vosecky J, et al. Personalized query suggestion with diversity awareness. Proceedings of the IEEE 30th International Conference on Data Engineering, Mar 31-Apr 4, 2014, Chicago, IL, USA. Piscataway, NJ, USA: IEEE, 2014: 400-411

17. Khosla A, An B, Lim J J, et al. Looking beyond the visible scene. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Jun 24-27, 2014, Columbus, OH, USA. Piscataway, NJ, USA: IEEE, 2014: 3710-3717

18. Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993-1022

19. Zhao W X, Jiang J, Weng J, et al. Comparing twitter and traditional media using topic models. Proceedings of the 33rd European Conference on Information Retrieval, Apr 18-21, 2011, Dublin, Ireland. LNCS 6611. Berlin, Germany: Springer, 2011: 338-349

20. Hu B, Jamali M, Ester M. Spatio-temporal topic modeling in mobile social media for location recommendation. Proceedings of the IEEE 13th International Conference on Data Mining, Dec 7-10, 2013, Dallas, TX, USA. Piscataway, NJ, USA: IEEE, 2013: 1073-1078

[1]	Zhang Jiangbo, Zheng Jiali, Quan Yixuan, Lin Zihan, Xie Xiaode. Improved sparrow search algorithm for RFID network planning[J]. 中国邮电高校学报(英文版), 2023, 30(1): 93-102.
[2]	谢孝德郑嘉利林子涵何思怡冯敏瑜. Random mating mayfly algorithm for RFID network planning[J]. 中国邮电高校学报(英文版), 2022, 29(5): 40-50.
[3]	林子涵郑嘉利谢孝德冯敏瑜何思怡. RFID network planning based on improved brain storm optimization algorithm[J]. 中国邮电高校学报(英文版), 2022, 29(5): 30-39.
[4]	Lv Lihui, Wang Hong, Ma Zhi, Duan Qianheng, Fei Yangyang, Meng Xiangdong. Methods for solving equations with errors based on the HHL algorithm[J]. 中国邮电高校学报(英文版), 2022, 29(4): 9-20.
[5]	Quan Yixuan, Zheng Jiali, Xie Xiaode, Lin Zihan, Luo Wencong. Hybrid gray wolf optimization-cuckoo search algorithm for RFID network planning [J]. 中国邮电高校学报(英文版), 2021, 28(6): 91-102.
[6]	李丽, 郑嘉利, 全艺璇, 林子涵, 李映超, 黄天星. RFID Indoor positioning based on Semi-supervised Actor-Critic Co-training[J]. 中国邮电高校学报(英文版), 2020, 27(5): 69-81.
[7]	Lü Chaohui, Pan Jiaying. Extraction technique of region of interest from stereoscopic video[J]. 中国邮电高校学报(英文版), 2017, 24(5): 68-76.
[8]	张小红胡应梦. RFID mutual-authentication protocol with synchronous updated-keys based on Hash function[J]. Acta Metallurgica Sinica(English letters), 2015, 22(6): 27-35.

Social media mining and visualization for point-of-interest recommendation

Social media mining and visualization for point-of-interest recommendation

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

编辑推荐

Metrics

本文评价