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A Unified Video Recommendation by Cross-Network User Modeling

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Published:03 August 2016Publication History
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Abstract

Online video sharing sites are increasingly encouraging their users to connect to the social network venues such as Facebook and Twitter, with goals to boost user interaction and better disseminate the high-quality video content. This in turn provides huge possibilities to conduct cross-network collaboration for personalized video recommendation. However, very few efforts have been devoted to leveraging users’ social media profiles in the auxiliary network to capture and personalize their video preferences, so as to recommend videos of interest. In this article, we propose a unified YouTube video recommendation solution by transferring and integrating users’ rich social and content information in Twitter network. While general recommender systems often suffer from typical problems like cold-start and data sparsity, our proposed recommendation solution is able to effectively learn from users’ abundant auxiliary information on Twitter for enhanced user modeling and well address the typical problems in a unified framework. In this framework, two stages are mainly involved: (1) auxiliary-network data transfer, where user preferences are transferred from an auxiliary network by learning cross-network knowledge associations; and (2) cross-network data integration, where transferred user preferences are integrated with the observed behaviors on a target network in an adaptive fashion. Experimental results show that the proposed cross-network collaborative solution achieves superior performance not only in terms of accuracy, but also in improving the diversity and novelty of the recommended videos.

References

  1. Fabian Abel, Samur Araújo, Qi Gao, and Geert-Jan Houben. 2011. Analyzing cross-system user modeling on the social web. In Web Engineering. Springer, 28--43. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Marko Balabanović and Yoav Shoham. 1997. Fab: Content-based, collaborative recommendation. Communications of the ACM 40, 3 (1997), 66--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Marko Balabanović and Yoav Shohom. 1997. Content-based, callaborative recommendation. Communications of the ACM 40, 3 (1997). Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Justin Basilico and Thomas Hofmann. 2004. Unifying collaborative and content-based filtering. In Proceedings of the 21st International Conference on Machine Learning. ACM, 9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. David M. Blei and Michael I. Jordan. 2003. Modeling annotated data. In SIGIR 2003. 127--134. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. The Journal of Machine Learning Research 3 (2003), 993--1022. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Robin Burke. 2002. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12, 4 (2002), 331--370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Gregory Camilli and Kenneth D. Hopkins. 1978. Applicability of chi-square to 2 × 2 contingency tables with small expected cell frequencies. Psychological Bulletin 85, 1 (1978), 163.Google ScholarGoogle ScholarCross RefCross Ref
  9. Iván Cantador and Paolo Cremonesi. 2014. Tutorial on cross-domain recommender systems. In Proceedings of the 8th ACM Conference on Recommender Systems. ACM, 401--402. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Youngchul Cha and Junghoo Cho. 2012. Social-network analysis using topic models. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 565--574. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Terence Chen, Mohamed Ali Kaafar, Arik Friedman, and Roksana Boreli. 2012. Is more always merrier? A deep dive into online social footprints. In Proceedings of the 2012 ACM Workshop on Workshop on Online Social Networks. ACM, 67--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Zhengyu Deng, Jitao Sang, and Changsheng Xu. 2013. Personalized video recommendation based on cross-platform user modeling. In ICME 2013. IEEE, 1--6.Google ScholarGoogle Scholar
  13. Maeve Duggan and Aaron Smith. 2013. Social media update 2013. Pew Internet and American Life Project (2013).Google ScholarGoogle Scholar
  14. Ignacio Fernández-Tobías, Iván Cantador, Marius Kaminskas, and Francesco Ricci. 2012. Cross-domain recommender systems: A survey of the state of the art. In Spanish Conference on Information Retrieval.Google ScholarGoogle Scholar
  15. Huiji Gao, Jalal Mahmud, Jilin Chen, Jeffrey Nichols, and Michelle Zhou. 2014. Modeling user attitude toward controversial topics in online social media. In 8th International AAAI Conference on Weblogs and Social Media.Google ScholarGoogle Scholar
  16. Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2015. Content-aware point of interest recommendation on location-based social networks. In Proceedings of the 29th AAAI Conference on Artificial Intelligence. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Shenghua Gao, Ivor Wai-Hung Tsang, Liang-Tien Chia, and Peilin Zhao. 2010. Local features are not lonely—Laplacian sparse coding for image classification. In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 3555--3561.Google ScholarGoogle ScholarCross RefCross Ref
  18. Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22, 1 (2004), 5--53. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Richang Hong and Ling Shao. 2012. Learning from social media network. Neurocomputing 95 (2012), 1--2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Richang Hong, Meng Wang, Yue Gao, Dacheng Tao, Xuelong Li, and Xindong Wu. 2014. Image annotation by multiple-instance learning with discriminative feature mapping and selection. IEEE Transactions on Cybernetics 44, 5 (2014), 669--680.Google ScholarGoogle ScholarCross RefCross Ref
  21. Richang Hong, Yang Yang, Meng Wang, and Xian-Sheng Hua. 2015. Learning visual semantic relationships for efficient visual retrieval. IEEE Transactions on Big Data 1, 4 (2015), 152--161.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 8th IEEE International Conference on Data Mining (ICDM’08). IEEE, 263--272. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. George Karypis. 2001. Evaluation of item-based top-n recommendation algorithms. In Proceedings of the 10th International Conference on Information and Knowledge Management. ACM, 247--254. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Yehuda Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 426--434. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Bin Li, Qiang Yang, and Xiangyang Xue. 2009a. Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction. In IJCAI, Vol. 9. 2052--2057. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Bin Li, Qiang Yang, and Xiangyang Xue. 2009b. Transfer learning for collaborative filtering via a rating-matrix generative model. In Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 617--624. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Babak Loni, Yue Shi, Martha Larson, and Alan Hanjalic. 2014. Cross-domain collaborative filtering with factorization machines. In Advances in Information Retrieval. Springer, 656--661.Google ScholarGoogle Scholar
  28. Viktor Mayer-Schönberger and Kenneth Cukier. 2013. Big Data: A Revolution that Will Transform How we Live, Work, and Think. Houghton Mifflin Harcourt.Google ScholarGoogle Scholar
  29. Tao Mei, Bo Yang, Xian-Sheng Hua, and Shipeng Li. 2011. Contextual video recommendation by multimodal relevance and user feedback. ACM Transactions on Information Systems (TOIS) 29, 2 (2011), 10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Andriy Mnih and Ruslan Salakhutdinov. 2007. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems. 1257--1264. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Jonghun Park, Sang-Jin Lee, Sung-Jun Lee, Kwanho Kim, Beom-Suk Chung, and Yong-Ki Lee. 2010. Online video recommendation through tag-cloud aggregation. IEEE MultiMedia 1 (2010), 78--87. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Michael J. Pazzani and Daniel Billsus. 2007. Content-based recommendation systems. In The Adaptive Web. Springer, 325--341. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Recommender Systems Handbook: A Complete Guide for Scientists and Practitioners. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Suman Deb Roy, Tao Mei, Wenjun Zeng, and Shipeng Li. 2012. Socialtransfer: Cross-domain transfer learning from social streams for media applications. In ACM Multimedia 2012. ACM, 649--658. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Jitao Sang, Zhengyu Deng, Dongyuan Lu, and Changsheng Xu. 2015. Cross-OSN user modeling by homogeneous behavior quantification and local social regularization. IEEE Transactions on Multimedia 17, 12 (2015), 2259--2270.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Guy Shani, Max Chickering, and Christopher Meek. 2008. Mining recommendations from the web. In Proceedings of the 2008 ACM Conference on Recommender Systems. ACM, 35--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Yue Shi, Martha Larson, and Alan Hanjalic. 2014. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Computing Surveys (CSUR) 47, 1 (2014), 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Ajit P. Singh and Geoffrey J. Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 650--658. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009 (2009), 4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Zhi Wang, Lifeng Sun, Wenwu Zhu, Shiqiang Yang, Hongzhi Li, and Dapeng Wu. 2013. Joint social and content recommendation for user-generated videos in online social network. IEEE Transactions on Multimedia 15, 3 (2013), 698--709. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Ming Yan, Jitao Sang, Tao Mei, and Changsheng Xu. 2013. Friend transfer: Cold-start friend recommendation with cross-platform transfer learning of social knowledge. In 2013 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1--6.Google ScholarGoogle Scholar
  42. Ming Yan, Jitao Sang, and Changsheng Xu. 2015. Unified YouTube video recommendation via cross-network collaboration. In Proceedings of the 5th ACM Conference on International Conference on Multimedia Retrieval. ACM, 19--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Bo Yang, Tao Mei, Xian-Sheng Hua, Linjun Yang, Shi-Qiang Yang, and Mingjing Li. 2007. Online video recommendation based on multimodal fusion and relevance feedback. In Proceedings of the 6th ACM International Conference on Image and Video Retrieval. ACM, 73--80. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th International Conference on World Wide Web. ACM, 22--32. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Transactions on Multimedia Computing, Communications, and Applications
        ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 12, Issue 4
        August 2016
        219 pages
        ISSN:1551-6857
        EISSN:1551-6865
        DOI:10.1145/2983297
        Issue’s Table of Contents

        Copyright © 2016 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 3 August 2016
        • Accepted: 1 May 2016
        • Revised: 1 January 2016
        • Received: 1 September 2015
        Published in tomm Volume 12, Issue 4

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