User-Aware Folk Popularity Rank: User-Popularity-Based Tag Recommendation That Can Enhance Social Popularity

ABSTRACT

In this paper we propose a method that can enhance the social popularity of a post (i.e., the number of views or likes) by recommending appropriate hash tags considering both content popularity and user popularity. A previous approach called FolkPopularityRank (FP-Rank) considered only the relationship among images, tags, and their popularity. However, the popularity of an image/video is strongly affected by who uploaded it. Therefore, we develop an algorithm that can incorporate user popularity and users' tag usage tendency into the FP-Rank algorithm. The experimental results using 60,000 training images with their accompanying tags and 1,000 test data, which were actually uploaded to a real social network service (SNS), show that, in ten days, our proposed algorithm can achieve 1.2 times more views than the FP-Rank algorithm. This technology would be critical to individual users and companies/brands who want to promote themselves in SNSs.

References

1. Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2018. Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6077--6086.
2. Adam Bielski and Tomasz Trzcinski. 2018. Pay Attention to Virality: understanding popularity of social media videos with the attention mechanism. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . 2335--2337.
3. Spencer Cappallo, Thomas Mensink, and Cees G.M. Snoek. 2015. Latent factors of visual popularity prediction. In Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR). 195--202.
4. Gang Chen, Ran Xu, and Zhi Yang. 2018. Deep ranking structural support vector machine for image tagging. Pattern Recognition Letters , Vol. 105 (2018), 30--38.
5. Jia Chen, Qin Jin, and Yifan Xiong. 2016a. Semantic Image Profiling for Historic Events: Linking Images to Phrases. In Proceedings of the 24th ACM International Conference on Multimedia (ACMMM) . 1028--1037.
6. Jingyuan Chen, Xuemeng Song, Liqiang Nie, Xiang Wang, Hanwang Zhang, and Tat-Seng Chua. 2016b. Micro Tells Macro: predicting the popularity of micro-Videos via a transductive model. In Proceedings of the ACM International Conference on Multimedia (ACMMM). 898--907.
7. Francesco Gelli, Tiberio Uricchio, Marco Bertini, Alberto Del Bimbo, and Shih-Fu Chang. 2015. Image popularity prediction in social media using sentiment and context features. In Proceedings of the ACM International Conference on Multimedia (ACMMM) . 907--910.
8. Jonathan Gemmell, Thomas Schimoler, Maryam Ramezani, Laura Christiansen, and Bamshad Mobasher. 2009. Improving Folkrank with item-based collaborative filtering. In Proceedings of the ACM RecSys Workshop on on Recommender Systems and the Social Web (RSWEB) . 17--24.
9. Daniela Godoy and Alejandro Corbellini. 2016. Folksonomy-based recommender systems: A state-of-the-art review. International Journal of Intelligent Systems , Vol. 31, 4 (2016), 314--346.
10. Jiuxiang Gu, Jianfei Cai, Gang Wang, and Tsuhan Chen. 2018. Stack-captioning: Coarse-to-fine learning for image captioning. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI) . 6837--6844.
11. Yahong Han, Xingxing Wei, Xiaochun Cao, Yi Yang, and Xiaofang Zhou. 2014. Augmenting image descriptions using structured prediction output. IEEE Transactions on Multimedia (TMM) , Vol. 16, 6 (2014), 1665--1676.
12. Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, and Kazunari Sugiyama. 2014. Predicting the popularity of web 2.0 items based on user comments. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. 233--242.
13. 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 (WWW). 173--182.
14. Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016. Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 549--558.
15. Andreas Hotho, Robert Jaschke, Christoph Schmitz, and Gerd Stumme. 2006. Folkrank: A ranking algorithm for folksonomies. In Proceedings of the LWA, Vol. 1. 111--114.
16. Jiani Hu, Toshihiko Yamasaki, and Kiyoharu Aizawa. 2016. Multimodal learning for image popularity prediction on social media. In Proceedings of the IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW) . 264--265.
17. Feitao Huang, Junhong Chen, Zehang Lin, Peipei Kang, and Zhenguo Yang. 2018. Random forest exploiting post-related and user-related features for social media popularity prediction. In Proceedings of the ACM International Conference on Multimedia (ACMMM). 2013--2017.
18. Fouzia Jabeen, Shah Khusro, Amna Majid, and Azhar Rauf. 2016. Semantics discovery in social tagging systems: A review. Multimedia Tools and Applications , Vol. 75, 1 (2016), 573--605.
19. Peiguang Jing, Yuting Su, Liqiang Nie, Xu Bai, Jing Liu, and Meng Wang. 2018. Low-rank multi-view embedding learning for micro-video popularity prediction. IEEE Transactions on Knowledge and Data Engineering , Vol. 30, 8 (2018), 1519--1532.
20. Joseph A Konstan, Bradley N Miller, David Maltz, Jonathan L Herlocker, Lee R Gordon, and John Riedl. 1997. GroupLens: applying collaborative filtering to Usenet news. Commun. ACM , Vol. 40, 3 (1997), 77--87.
21. Girish Kulkarni, Visruth Premraj, Vicente Ordonez, Sagnik Dhar, Siming Li, Yejin Choi, Alexander C Berg, and Tamara L Berg. 2013. Babytalk: Understanding and generating simple image descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. 35, 12 (2013), 2891--2903.
22. Nikolas Landia, Sarabjot Singh Anand, Andreas Hotho, Robert Jaschke, Stephan Doerfel, and Folke Mitzlaff. 2012. Extending FolkRank with content data. In Proceedings of the 4th ACM RecSys workshop on Recommender Systems and the Social Web. 1--8.
23. Patrick Le Callet, Sebastian Möller, Andrew Perkis, et almbox. 2012. Qualinet white paper on definitions of quality of experience. European network on quality of experience in multimedia systems and services (COST Action IC 1003) , Vol. 3 (2012).
24. Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, and Xiaodong He. 2018. Stacked Cross Attention for Image-Text Matching. In Proceedings of the European Conference on Computer Vision (ECCV). 212--228.
25. Zehang Lin, Feitao Huang, Yukun Li, Zhenguo Yang, and Wenyin Liu. 2018. A layer-wise deep stacking model for social image popularity prediction. Journal of World Wide Web , Vol. 22, 4 (2018), 1--17.
26. Pasquale Lops, Marco de Gemmis, Giovanni Semeraro, Cataldo Musto, and Fedelucio Narducci. 2013. Content-based and collaborative techniques for tag recommendation: an empirical evaluation. Journal of Intelligent Information Systems , Vol. 40, 1 (2013), 41--61.
27. Tinghuai Ma, Jinjuan Zhou, Meili Tang, Yuan Tian, Abdullah Al-Dhelaan, Mznah Al-Rodhaan, and Sungyoung Lee. 2015. Social network and tag sources based augmenting collaborative recommender system. IEICE Transactions on Information and Systems , Vol. 98, 4 (2015), 902--910.
28. Eric Massip, Shintami Chusnul Hidayati, Wen-Huang Cheng, and Kai-Lung Hua. 2018. Exploiting category-specific information for image popularity prediction in social media. In Proceedings of the IEEE International Conference on Multimedia & Expo Workshops (ICMEW). 45--46.
29. Philip J. McParlane, Yashar Moshfeghi, and Joemon M. Jose. 2014. "Nobody comes here anymore, It's too crowded"; Predicting image popularity on Flickr. In Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR). 385:385--385:391.
30. Mayank Meghawat, Satyendra Yadav, Debanjan Mahata, Yifang Yin, Rajiv Ratn Shah, and Roger Zimmermann. 2018. A multimodal approach to predict social media popularity. In Proceedings of the IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) . 190--195.
31. Swapnil Mishra, Marian-Andrei Rizoiu, and Lexing Xie. 2018. Modeling Popularity in Asynchronous Social Media Streams with Recurrent Neural Networks. In Proceedings of the Twelfth International Conference on Web and Social Media (ICWSM). 201--210.
32. Hanh T. H. Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rego Drumond, and Lars Schmidt-Thieme. 2017. Personalized Deep Learning for Tag Recommendation. In Proceedings of the Pacific-Asia Conference on knowledge discovery and data mining. 186--197.
33. Amandianeze O. Nwana, Amir Salman Avestimehr, and Tsuhan Chen. 2013. A latent social approach to YouTube popularity prediction. In Proceedings of the IEEE Global Communications Conference (GLOBECOM). 3138--3144.
34. Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report 1999--66. Stanford Digital Library Technologies Project. http://ilpubs.stanford.edu:8090/422/
35. Priyanka Rathord, Anurag Jain, and Chetan Agrawal. 2019. A comprehensive review on online news popularity prediction using machine learning approach. International Journal Online of Science , Vol. 5, 1 (2019), 7--7.
36. Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the ACM conference on Computer Supported Cooperative Work . 175--186.
37. Jalal Rezaeenour, Mansoureh Yari Eili, Esmaeil Hadavandi, and Mohammad Hossein Roozbahani. 2018. Developing a new hybrid intelligent approach for prediction online news popularity. International Journal of Information Science and Management (IJISM) , Vol. 16, 1 (2018), 71--87.
38. KC Santosh, Sohan De Sarkar, and Arjun Mukherjee. 2018. Product Popularity Modeling Via Time Series Embedding. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 650--653.
39. Xiance Si, Zhiyuan Liu, Peng Li, Qixia Jiang, and Maosong Sun. 2009. Content-based and graph-based tag suggestion. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) Discovery Challenge. 243--260.
40. B. Sigurbjörnsson and R. van Zwol. 2008. Flickr tag recommendation based on collective knowledge. In Proceedings of the World Wide Web Conference on World Wide Web (WWW). 327--336.
41. Ayush Singhal, Pradeep Sinha, and Rakesh Pant. 2017. Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works. International Journal of Computer Applications , Vol. 180, 7 (2017), 17--22.
42. Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence , Vol. 2009 (2009), 4:2--4:2.
43. Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. 2016. YFCC100M: The New Data in Multimedia Research. Commun. ACM , Vol. 59, 2 (2016), 64--73.
44. Roelof van Zwol, Adam Rae, and Lluis Garcia Pueyo. 2010. Prediction of favourite photos using social, visual, and textual signals. In Proceedings of the ACM International Conference on Multimedia (ACMMM) . 1015--1018.
45. Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '15). 1235--1244.
46. Xueting Wang, Yu Enokibori, Takatsugu Hirayama, Kensho Hara, and Kenji Mase. 2017. User Group Based Viewpoint Recommendation Using User Attributes for Multiview Videos. In Proceedings of the Workshop on Multimodal Understanding of Social, Affective and Subjective Attributes (MUSA2 '17). 3--9.
47. Baoyuan Wu, Weidong Chen, Peng Sun, Weiwei Liu, Bernard Ghanem, and Siwei Lyu. 2018. Tagging Like Humans: Diverse and Distinct Image Annotation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 7967--7975.
48. Bo Wu, Wen-Huang Cheng, Yongdong Zhang, and Tao Mei. 2016. Time Matters: Multi-scale temporalization of social media popularity. In Proceedings of the ACM International Conference on Multimedia (ACMMM) . 1336--1344.
49. Kota Yamaguchi, Tamara L Berg, and Luis E Ortiz. 2014. Chic or social: Visual popularity analysis in online fashion networks. In Proceedings of the 22nd ACM International Conference on Multimedia (ACMMM) . 773--776.
50. Toshihiko Yamasaki, Jiani Hu, Shumpei Sano, and Kiyoharu Aizawa. 2017. Folkpopularityrank: Tag recommendation for enhancing social popularity using text tags in content sharing services. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI). 3231--3237.
51. Toshihiko Yamasaki, Shumpei Sano, and Kiyoharu Aizawa. 2014. Social popularity score: Predicting numbers of views, comments, and favorites of social photos using only annotations. In Proceedings of the International Workshop on Internet-Scale Multimedia Management (WISMM). 3--8.
52. Hong Yu, Bing Zhou, Mingyao Deng, and Feng Hu. 2018. Tag recommendation method in folksonomy based on user tagging status. Journal of Intelligent Information Systems (2018), 1--22.
53. Wei Zhang, Wen Wang, Jun Wang, and Hongyuan Zha. 2018. User-guided hierarchical attention network for multi-modal social image popularity prediction. In Proceedings of the International Conference on World Wide Web (WWW). 1277--1286.
54. Yang Zhang, Boqing Gong, and Mubarak Shah. 2016. Fast zero-shot image tagging. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 5985--5994.
55. Yuan Zhang, Ning Zhang, and Jie Tang. 2009. A collaborative filtering tag recommendation system based on graph. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) Discovery Challenge . 297--306.
56. Yi Zhu, Alan Hanjalic, and Judith A Redi. 2016. QoE prediction for enriched assessment of individual video viewing experience. In Proceedings of the 24th ACM International Conference on Multimedia (ACMMM). 801--810.

Index Terms

• Human-centered computing

  • Collaborative and social computing

• Information systems

  • Information systems applications

    • Collaborative and social computing systems and tools

      • Social networking sites