Abstract
Traditional personalized video recommendation methods focus on utilizing user profile or user history behaviors to model user interests, which follows a static strategy and fails to capture the swift shift of the short-term interests of users. According to our cross-platform data analysis, the information emergence and propagation is faster in social textual stream-based platforms than that in multimedia sharing platforms at micro user level. Inspired by this, we propose a dynamic user modeling strategy to tackle personalized video recommendation issues in the multimedia sharing platform YouTube, by transferring knowledge from the social textual stream-based platform Twitter. In particular, the cross-platform video recommendation strategy is divided into two steps. (1) Real-time hot topic detection: the hot topics that users are currently following are extracted from users' tweets, which are utilized to obtain the related videos in YouTube. (2) Time-aware video recommendation: for the target user in YouTube, the obtained videos are ranked by considering the user profile in YouTube, time factor, and quality factor to generate the final recommendation list. In this way, the short-term (hot topics) and long-term (user profile) interests of users are jointly considered. Carefully designed experiments have demonstrated the advantages of the proposed method.
- F. Abel, S. Araújo, Q. Gao, and G. Houben. 2011. Analyzing cross-system user modeling on the social web. Web Engin. 28--43. Google Scholar
Digital Library
- F. Abel, Q. Gao, G. Houben, and K. Tao. 2011a. Analyzing user modeling on Twitter for personalized news recommendations. In User Modeling, Adaption and Personalization, J. Konstan, R. Conejo, J. Marzo, and N. Oliver, Eds., 1--12. Google Scholar
Digital Library
- F. Abel, Q. Gao, G. Houben, and K. Tao. 2011b. Semantic enrichment of Twitter posts for user profile construction on the social web. In The Semantic Web: Research and Applications, 375--389. Google Scholar
Digital Library
- F. Abel, Q. Gao, G.-J. Houben, and K. Tao. 2011c. Analyzing temporal dynamics in twitter profiles for personalized recommendations in the social web. In Proceedings of the 3rd ACM International Conference on Web Science (WebSci). Google Scholar
Digital Library
- Y.-Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong. 2007. Analysis of topological characteristics of huge online social networking services. In Proceedings of the 16th International Conference on World Wide Web. 835--844. Google Scholar
Digital Library
- S. Baluja, R. Seth, D. Sivakumar, Y. Jing, J. Yagnik, S. Kumar, D. Ravichandran, and M. Aly. 2008. Video suggestion and discovery for YouTube: taking random walks through the view graph. In Proceedings of the WWW Conference. 895--904. Google Scholar
Digital Library
- F. Benevenuto, T. Rodrigues, V. Almeida, J. Almeida, and K. Ross. 2009. Video interactions in online video social networks. ACM Trans. Multimedia Comput. Commun. Appl. 5, 4, 30. Google Scholar
Digital Library
- P. N. Bennett, R. W. White, W. Chu, S. T. Dumais, P. Bailey, F. Borisyuk, and X. Cui. 2012. Modeling the impact of short- and long-term behavior on search personalization. In Proceedings of ACM SIGIR. 185--194. Google Scholar
Digital Library
- J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston, and D. Sampath. 2010. The YouTube video recommendation system. In Proceedings of RecSys. 293--296. Google Scholar
Digital Library
- Z. Deng, J. Sang, and C. Xu. 2013. Personalized video recommendation based on cross-platform user modeling. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME).Google Scholar
- B. Fortuna, D. Mladenic, and M. Grobelnik. 2011. User modeling combining access logs, page content and semantics. Arxiv preprint arXiv:1103.5002.Google Scholar
- Q. Gao, F. Abel, G. Houben, and K. Tao. 2011. Interweaving trend and user modeling for personalized news recommendation. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). Vol. 1, 100--103. Google Scholar
Digital Library
- Y. Gao, M. Wang, Z.-J. Zha, J. Shen, X. Li, and X. Wu. 2013. Visual-textual joint relevance learning for tag-based social image search.IEEE Trans. Image Process. 22, 1, 363--376. Google Scholar
Digital Library
- Y. Jin, M. Hu, H. Singh, D. Rule, M. Berlyant, and Z. Xie. 2010. Myspace video recommendation with map-reduce on qizmt. In Proceedings of the IEEE 4th International Conference on Semantic Computing (ICSC). 126--133. Google Scholar
Digital Library
- N. Koenigstein, G. Dror, and Y. Koren. 2011. Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In Proceedings of the 5th ACM Conference on Recommender systems. 165--172. Google Scholar
Digital Library
- Y. Koren. 2010. Collaborative filtering with temporal dynamics. Commun. ACM 53, 4, 89--97. Google Scholar
Digital Library
- H. Kwak, C. Lee, H. Park, and S. B. Moon. 2010. What is Twitter, a social network or a news media? In Proceedings of the WWW Conference. 591--600. Google Scholar
Digital Library
- K. Lerman and R. Ghosh. 2010. Information contagion: An empirical study of the spread of news on Digg and Twitter social networks. CoRR abs/1003.2664.Google Scholar
- A. Liu, Y. Zhang, and J. Li. 2009. Personalized movie recommendation. In Proceedings of the 17th ACM International Conference on Multimedia. 845--848. Google Scholar
Digital Library
- M. Magnani and L. Rossi. 2011. The ml-model for multi-layer social networks. In Proceedings of IEEE International Conference on Advances in Social Networks Analysis and Mining. 5--12. Google Scholar
Digital Library
- T. Mei, B. Yang, X.-S. Hua, and S. Li. 2011. Contextual video recommendation by multimodal relevance and user feedback. ACM Trans. Inf. Syst. 29, 2, 1--24. Google Scholar
Digital Library
- M. Osborne, S. Petrovic, R. Mccreadie, C. Macdonald, and I. Ounis. 2012. Bieber no more: First story detection using Twitter and Wikipedia. In Proceedings of the SIGIR Workshop on Time-Aware Information Access.Google Scholar
- J. Park, S. Lee, K. Kim, B. Chung, and Y. Lee. 2011. An online video recommendation framework using view based tag cloud aggregation. IEEE Multimedia 18, 1. Google Scholar
Digital Library
- S. Roy, T. Mei, W. Zeng, and S. Li. 2012. Socialtransfer: Cross-domain transfer learning from social streams for media applications. In Proceedings of the 20th ACM International Conference on Multimedia. 649--658. Google Scholar
Digital Library
- J. Sang and C. Xu. 2012. Right buddy makes the difference: An early exploration of social relation analysis in multimedia applications. In Proceedings of the 20th ACM International Conference on Multimedia. 19--28. Google Scholar
Digital Library
- M. Szomszor, H. Alani, I. Cantador, K. Ohara, and N. Shadbolt. 2008a. Semantic modelling of user interests based on cross-folksonomy analysis. In Proceedings of the IEEE/WIC/ACM International Conference on the Semantic Web. 632--648. Google Scholar
Digital Library
- M. Szomszor, I. Cantador, and H. Alani. 2008b. Correlating user profiles from multiple folksonomies. In Proceedings of the 19th ACM Conference on Hypertext and Hypermedia. 33--42. Google Scholar
Digital Library
- J. Wang, E. Chng, C. Xu, H. Lu, and Q. Tian. 2007. Generation of personalized music sports video using multimodal cues.IEEE Trans. Multimedia 9, 3, 576--588. Google Scholar
Digital Library
- J. Weng, E.-P. Lim, J. Jiang, and Q. He. 2010. Twitterrank : Finding topic-sensitive influential twitterers. In Proceedings of WSDM. 261--270. Google Scholar
Digital Library
- L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, and J. Sun. 2010. Temporal recommendation on graphs via long- and short-term preference fusion. In Proceedings of KDD. 723--732. Google Scholar
Digital Library
- L. Xiong, X. Chen, T.-K. Huang, J. G. Schneider, and J. G. Carbonell. 2010. Temporal collaborative filtering with Bayesian probabilistic tensor factorization. In Proceedings of SIAM SDM. 211--222.Google Scholar
- M. Yan, J. Sang, T. Mei, and C. Xu. 2013. Friend transfer: Cold-start friend recommendation with cross-platform transfer learning of social knowledge. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME). 1--6.Google Scholar
- D. Yang, T. Chen, W. Zhang, Q. Lu, and Y. Yu. 2012. Local implicit feedback mining for music recommendation. In Proceedings of the 6th ACM Conference on Recommender Systems (RecSys). 91--98. Google Scholar
Digital Library
- J. Yang and J. Leskovec. 2011. Patterns of temporal variation in online media. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 177--186. Google Scholar
Digital Library
- W. X. Zhao, J. Jiang, J. Weng, J. He, E.-P. Lim, H. Yan, and X. Li. 2011. Comparing Twitter and traditional media using topic models. In Proceedings of ECIR. 338--349. Google Scholar
Digital Library
- X. Zhao, G. Li, M. Wang, J. Yuan, Z.-J. Zha, Z. Li, and T.-S. Chua. 2011. Integrating rich information for video recommendation with multi-task rank aggregation. In Proceedings of the 19th ACM International Conference on Multimedia. 1521--1524. Google Scholar
Digital Library
Index Terms
Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube
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