Papers can be downloaded from my Google Scholar page.

Publications

[1] Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, and Jun Wang. Long text generation via adversarial training with leaked information, 2017.

[2] Lianmin Zheng, Jiacheng Yang, Han Cai, Weinan Zhang, Jun Wang, and Youg Yu. Magent: A many-agent reinforcement learning research platform for artificial collective intelligence. In NIPS 2017 Demo, 2017.

[3] Peng Peng, Quan Yuan, Ying Wen, Yaodong Yang, Zhenkun Tang, Haitao Long, and Jun Wang. Multiagent bidirectionally-coordinated nets: Emergence of human-level coordina- tion in learning to play starcraft combat games. arXiv preprint arXiv:1703.10069, 2017.

[4] Yaodong Yang, Lantao Yu, Yiwei Bai, Jun Wang, Weinan Zhang, Ying Wen, and Yong Yu. An empirical study of AI population dynamics with million-agent reinforcement learning. arXiv preprint, 2017.

[5] Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang. Reinforcement learning for architecture search by network transformation. arXiv preprint arXiv:1707.04873, 2017.

[6] Yaodong Yang; Sergey Demyanov; Yunayuan Liu; Jun Wang. Adversarial variational infer- ence for tweedie compound poisson models. ICML2017 Implicit Models Workshop, 2017.

[7] Haifeng Zhang, Jun Wang, Zhiming Zhou, Weinan Zhang, Ying Wen, Yong Yu, and Wenxin Li. Learning to design games: Strategic environments in deep reinforcement learning. arXiv preprint arXiv:1707.01310, 2017.

[8] Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. IRGAN: A minimax game for unifying generative and discriminative in- formation retrieval models. ACM SIGIR, 2017.

[9] Yiyang Li; Guanyu Tao; Weinan Zhang; Jun Wang; Yong Yu. Content recommendation by noise contrastive transfer learning of feature representation. ACM CIKM, 2017.

[10] Xuejian Wang, Lantao Yu, Kan Ren, Guanyu Tao, Weinan Zhang, Yong Yu, and Jun Wang. Dynamic attention deep model for article recommendation by learning human editors’ demonstration. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’17, pages 2051–2059, New York, NY, USA, 2017. ACM.

[11] L Yu, W Zhang, J Wang, and Y Yu. Seqgan: sequence generative adversarial nets with policy gradient. In AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence, volume 31. Association for the Advancement of Artificial Intelligence, 2017.

[12] Zhiming Zhou, Shu Rong, Han Cai, Weinan Zhang, Yong Yu, and Jun Wang. Generative adversarial nets with labeled data by activation maximization. In arXiv:1703.02000, 2017.

[13] Han Cai, Kan Ren, Weinan Zhang, Kleanthis Malialis, Jun Wang, Yong Yu, and Defeng Guo. Real-time bidding by reinforcement learning in display advertising. In The Tenth ACM International Conference on Web Search and Data Mining (WSDM). Association for Computing Machinery (ACM), 2017.

[14] H Zhang, W Zhang, J Wang, and Y Rong. Managing risk of bidding in display advertising. In The Tenth ACM International Conference on Web Search and Data Mining (WSDM). Associa- tion for Computing Machinery (ACM), 2017.

[15] Jun Wang, Weinan Zhang, and Shuai Yuan. Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting. Foundations and Trends in Information Retrieval, Now Publishers, 2017, 2017.

[16] Yanru Qu Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. Product-based neural networks for user response prediction. In ICDM. IEEE, 2016.

[17] RenKan,WeinanZhang,YifeiRong,HaifengZhang,YongYu,andJunWang.Userresponse learning for directly optimizing campaign performance in display advertising. In ACM CIKM, 2016.

[18] Weinan Zhang, Yifei Rong, Jun Wang, Tianchi Zhu, and Xiaofan Wang. Feedback control of real-time display advertising. In ACM WSDM. ACM, 2016.

[19] Weinan Zhang, Tianxiong Zhou, Jun Wang, and Jian Xu. Bid-aware gradient descent for unbiased learning with censored data in display advertising. In ACM KDD. ACM, 2016.

[20] Dell Zhang, Jun Wang, Emine Yilmaz, and Zhou Yuxin Wang, Xiaoling. Bayesian perfor- mance comparison of text classifiers. In ACM SIGIR. ACM, 2016.

[21] Weinan Zhang, Lingxi Chen, and Jun Wang. Implicit look-alike modelling in display ads: Transfer collaborative filtering to ctr estimation. In ECIR, 2016.

[22] Weinan Zhang, Tianming Du, and Jun Wang. Deep learning over multi-field categorical data: A case study on user response prediction. In ECIR, 2016.

[23] Ying Wen, Rui Luo Zhang, Weinan: Luo, and Jun Wang. Learning text representation using recurrent convolutional neural network with highway layers. In Neu-IR 2016. ACM, 2016.

[24] Kleanthis Malialis, Jun Wang, Gary Brooks, and George Frangou. Feature selection as a multiagent coordination problem. In ALA-16. ACM, 2016.

[25] Yuchen Wang, Kan Ren, Weinan Zhang, Jun Wang, and Yong Yu. Functional bid landscape forecasting for display advertising. In ECML-PKDD 2016, 2016.

[26] GraceHuiYang,MarcSloan,andJunWang.DynamicInformationRetrievalModeling.Morgan & Claypool, 2016.

[27] Bowei Chen, Jun Wang, Ingemar J Cox, and Mohan S Kankanhalli. Multi-keyword multi- click option contracts for sponsored search advertising. ACM Transactions on Intelligent Sys- tems and Technology, 2015.

[28] Marc Sloan, Hui Yang, and Jun Wang. A term-based methodology for query reformulation understanding. Information Retrieval Journal, 2015.

[29] Qian Yu, Peng Zhang, Yuexian Hou, Dawei Song, and Jun Wang. Document boltzmann machines for information retrieval. In European Conference on Information Retrieval, pages 666–671. Springer International Publishing, 2015.

[30] Hui Yang, Marc Sloan, and Jun Wang. Dynamic information retrieval modeling. In Proceed- ings of the Eighth ACM International Conference on Web Search and Data Mining, pages 409–410. ACM, 2015.

[31] Jun Wang and Shuai Yuan. Real-time bidding: A new frontier of computational advertising research. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pages 415–416. ACM, 2015.

[32] Pablo Castells, Jun Wang, Rubén Lara, and Dell Zhang. Introduction to the special issue on diversity and discovery in recommender systems. ACM Transactions on Intelligent Systems and Technology (TIST), 5(4):52, 2015.

[33] DellZhang,JunWang,andXiaoxueZhao.Estimatingtheuncertaintyofaveragef1scores.In ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2015), pages 317–320. ACM, 2015.

[34] Xiaoxue Zhao and Jun Wang. A theoretical analysis of a two-stage recommendation process for cold-start collaborative filtering. In ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2015). AcM, 2015.

[35] Mark Sloan and Jun Wang. Dynamic information retrieval: Theoretical framework and ap- plication. In ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2015). ACM, 2015.

[36] Weinan Zhang and Jun Wang. Statistical arbitrage mining for display advertising. In ACM KDD’15. ACM, 2015.

[37] Xiaoxue Zhao, Weinan Zhang, and Jun Wang. Risk-hedged venture capital investment rec- ommendation. In ACM RecSys ’15, page 10. ACM, 2015.

[38] Dell Zhang, Jun Wang, Xiaoxue Zhao, and Xiaoling Wang. A bayesian hierarchical model for comparing average f1 scores. In ICDM, 2015.

[39] Weinan Zhang, Shuai Yuan, Jun Wang, and Xuehua Shen. Real-time bidding benchmarking with ipinyou dataset. arXiv preprint arXiv:1407.7073, 2014.

[40] Peng Zhang, Linxue Hao, Dawei Song, Jun Wang, Yuexian Hou, and Bin Hu. Generalized bias-variance evaluation of trec participated systems. In Proceedings of the 23rd ACM Inter- national Conference on Conference on Information and Knowledge Management, pages 1911–1914. ACM, 2014.

[41] Bowei Chen, Shuai Yuan, and Jun Wang. A dynamic pricing model for unifying program- matic guarantee and real-time bidding in display advertising. In Proceedings of the Eighth In- ternational Workshop on Data Mining for Online Advertising, Best Paper Award, ADKDD’14, pages 1:1–1:9, New York, NY, USA, 2014. ACM.

[42] Hui Yang, Marc Sloan, and Jun Wang. Dynamic information retrieval modeling (tutorial). In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 1290–1290. ACM, 2014.

[43] Dawei Song Jun Wang Yuexian Hou Bin Hu Peng Zhang, Linxue Hao. Generalized bias- variance evaluation of trec participated systems. In CIKM Short Paper. ACM, 2014.

[44] PengZhang,DaweiSong,JunWang,andYuexianHou.Bias–varianceanalysisinestimating true query model for information retrieval. Information Processing & Management, 50(1):199– 217, 2014.

[45] BoweiChenShuaiYuan,JunWang.Anempiricalstudyofreservepriceoptimisationinreal- time bidding. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.

[46] Weinan Zhang, Shuai Yuan, and Jun Wang. Optimal real-time bidding for display advertis- ing. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.

[47] Xiaoxue Zhao, Weinan Zhang, and Jun Wang. Interactive collaborative filtering. In CIKM, 2013.

[48] Weinan Zhang, Jun Wang, Bowei Chen, and Xiaoxue Zhao. To personalize or not: A risk management perspective. In ACM RecSys, 2013.

[49] Shuai Yuan, Jun Wang, and Xiaoxue Zhao. Real-time bidding for online advertising: Mea- surement and analysis. In AdKDD, 2013.

[50] Shuai Yuan, Jun Wang, and Xiaoxue Zhao. Adaptive keywords extraction with contextual bandits for advertising on parked domains. In IATAP, 2013.

[51] Xiaoran Jin, Marc Sloan, and Jun Wang. Interactive exploratory search for multi page search results. In WWW, 2013.

[52] Jagadeesh Gorla, Neal Lathia, Stephen Robertson, and Jun Wang. Probabilistic group rec- ommendation via information matching. In WWW, 2013.

[53] Marc Sloan and Jun Wang. Iterative expectation for multi period information retrieval. In WSDM Workshop on Web Search Click Data, 2013.

[54] Alejandro Bellogín, Jun Wang, and Pablo Castells. Bridging memory-based collaborative filtering and text retrieval. Information retrieval, 16(6):697–724, 2013.

[55] Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu. Optimizing top-n collaborative filter- ing via dynamic negative item sampling. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pages 785–788. ACM, 2013.

[56] Shuai Yuan, Ahmad Zainal Abidin, Marc Sloan, and Jun Wang. Internet advertising: An interplay among advertisers, online publishers, ad exchanges and web users. arXiv preprint arXiv:1206.1754, 2012.

[57] C. Macdonald, J. Wang, and C. Clarke. 2nd international workshop on diversity in docu- ment retrieval (ddr 2012). In Proceedings of the fifth ACM international conference on Web search and data mining, pages 769–770. ACM, 2012.

[58] C. Macdonald, C. Clarke, and J. Wang. The 1st international workshop on diversity in doc- ument retrieval. ACM SIGIR Forum, 45(2):87–93, 2012.

[59] Alejandro Bellogin, Jun Wang, and Pablo Castells. Bridging memory-based collaborative filtering and text retrieval. In Journal of Information Retrieval, 2012.

[60] Shuai Yuan and Jun Wang. Sequential selection of correlated ads by POMDPs. In CIKM, 2012.

[61] Yue Shi, Xiaoxue Zhao, Jun Wang, Martha Larsona, and Alan Hanjalic. Adaptive diversifi- cation of recommendation results via latent factor portfolio. In SIGIR, 2012.

[62] M.SloanandJ.Wang.Dynamicalinformationretrievalmodelling:Aportfolio-armedbandit machine approach. In WWW Poster, 2012.

[63] J. Wang and B. Chen. Selling futures online advertising slots via option contracts. In WWW Poster, 2012.

[64] T.Jambor,J.Wang,andN.Lathia.Usingcontroltheoryforstableandefficientrecommender systems. In WWW, 2012.

[65] Guido Zuccon, Leif Azzopardi, Dell Zhang, and Jun Wang. Top-k retrieval using facility location analysis. In ECIR Best Paper Award, 2012.

[66] JunWangandKevynCollins-Thompson.Cikm2011tutorial:Statisticalinformationretrieval modelling: From probability ranking principle to recent advances in diversity, portfolio the- ory, and beyond. In CIKM, 2011.

[67] Alejandro Bellogin, Jun Wang, and Pablo Castells. Text retrieval methods for item ranking in collaborative filtering. In ECIR, 2011.

[68] Jun Wang and Kevyn Collins-Thompson. Ecir2011 tutorial: Risk management in informa- tion retrieval. In ECIR, 2011.

[69] Peng Zhang, Dawei Song, Jun Wang, Xiaozhao Zhao, and Yuexian Hou. On modeling rank- independent risk in estimating probability of relevance. In Asia Information Retrieval Sympo- sium, pages 13–24. Springer Berlin Heidelberg, 2011.

[70] Gorla. Jagadeesh, S.E. Robertson, and Jun Wang. A unified relevance retrieval model by eliteness hypothesis. ArXiv e-prints, http:arxiv.orgabs1106.2946, 2011.

[71] Tamas Jambor and Jun Wang. Optimizing multiple objectives in collaborative filtering. In ACM Recommender Systems, 2010.

[72] Ahmad Zainal-Abidin and Jun Wang. Maximizing clicks of sponsored search by integer programming. In the Fourth International Workshop on Data Mining and Audience Intelligence for Online Advertising, 2010.

[73] Jun Wang and Jianhan Zhu. On statistical analysis and optimization of information retrieval effectiveness metrics. In Proc. of the Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR), 2010.

[74] Dell Zhang, Jun Wang, Deng Cai, and Jinsong Lu. Self-taught hashing for fast similarity search. In Proc. of the Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR), 2010.

[75] Tamas Jambor and Jun Wang. Goal-driven collaborative filtering: A directional error based approach. In Proc. of European Conference on Information Retrieval (ECIR 2010). Oral Presenta- tion, 2010.

[76] Jun Wang. Language models of collaborative filtering. In the Fifth Asian Information Retrieval Symposium (AIRS 2009), 2009.

[77] Jun Wang and Jianhan Zhu. Portfolio theory of information retrieval. In Proc. of the An- nual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR), 2009.

[78] Jianhan Zhu, Jun Wang, Michael Taylor, and Ingemar Cox. Risky business: Modeling and exploiting uncertainty in information retrieval. In Proc. of the Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR), 2009.

[79] Jianhan Zhu, Jun Wang, and Vishwa Vinay. Topic (query) selection for ir evaluation. In Proc. of the Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR), 2009.

[80] Jun Wang. Mean-variance analysis: A new document ranking theory in information re- trieval. In Proc. of European Conference on Information Retrieval (ECIR 2009), Best Paper Award, 2009.

[81] Jianhan Zhu, Jun Wang, Michael J Taylor, and Ingemar Cox. Risk-aware information re- trieval. In Proc. of European Conference on Information Retrieval (ECIR 2009), 2009.

[82] Jun Wang, Johan Pouwelse, Jenneke Fokker, Marcel J.T. Reinders, and Arjen Paul de Vries. Personalization of a peer-to-peer television system. Book Chapter in Handbook of Digital Media in Entertainment and Arts, 2009.

[83] J. Wang, J. Yang, M. Clements, A.P. de Vries, and M.J.T. Reinders. Personalized collaborative tagging. Information Processing & Management, 2009.

[84] JunWang,ShiZhou,andDellZhang.Bridgingthegap:complexnetworksmeetinformation and knowledge management. In Proceeding of the 18th ACM conference on Information and knowledge management, CIKM ’09, pages 2113–2114, New York, NY, USA, 2009. ACM.

[85] Jun Wang. Relevance Models for Collaborative Filtering. Delft University of Technology, ISBN 978-90-9022932-4, 2008.

[86] J. Wang, S.E. Robertson, A.P. de Vries, and M.J.T. Reinders. Probabilistic relevance models for collaborative filtering. Information Retrieval, 11(6):477–497, 2008.

[87] Jun Wang, Arjen P. de Vries, and Marcel J.T. Reinders. Unified relevance models for rating prediction in collaborative filtering. ACM Trans. on Information System (TOIS), 2008.

[88] M. J.T. Reinders Jun Wang, A. P. de Vries. Wi-fi walkman. Book Chapter in Encyclopedia of Wireless and Mobile Communications, 2008.

[89] J.A.Pouwelse,P.Garbacki,J.Wang,A.Bakker,J.Yang,A.Iosup,D.H.J.Epema,M.Reinders, M. van Steen, and H.J. Sips. Tribler: A social-based peer-to-peer system. Concurrency and Computation: Practice and Experience, 2007.

[90] Maarten Clements, Arjen P. de Vries, Johan A. Pouwelse, Jun Wang, and Marcel J.T. Rein- ders. Evaluation of neighbourhood selection methods in decentralized recommendation systems. In Workshop on Large Scale Distributed Systems for Information Retrieval (LSDS-IR) in SIGIR07, 2007.

[91] J. Yang, J. Wang, M. Clements, J. Pouwelse, A.P. de Vries, and M.J.T Reinders. An epidemic- based P2P recommender system. In Workshop on Large Scale Distributed Systems for Informa- tion Retrieval (LSDS-IR) in SIGIR07, 2007.

[92] Jun Wang, Arjen P. de Vries, and Marcel J.T. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proc. of the 29th Annual Interna- tional ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR06), 2006.

[93] Jun Wang, Johan Pouwelse, Jenneke Fokker, Marcel J.T. Reinders, and Arjen Paul de Vries. Personalization of a peer-to-peer television system. In Proc. of European Conference on Inter- active Television (EuroITV 2006), 2006.

[94] Jun Wang, Arjen P. de Vries, and Marcel J.T. Reinders. A user-item relevance model for log- based collaborative filtering. In Proc. of European Conference on Information Retrieval (ECIR 2006), 2006.

[95] Jun Wang, Johan Pouwelse, Reginald Lagendijk, and Marcel R. J. Reinders. Distributed collaborative filtering for peer-to-peer file sharing systems. In Proc. of the 21st Annual ACM Symposium on Applied Computing, 2006.

[96] J.Pouwelse, P.Garbacki, J.Wang, A.Bakker, J.Yang, A.Iosup, D.Epema, M.Reinders, M. van Steen, and H.Sips. Tribler: A social-based peer-to-peer system. In Proc. of the 5th International Workshop on Peer-to-Peer Systems (IPTPS’06), 2006.

[97] Mohan Kankanhalli, Jun Wang, and Ramesh Jain. Experiential sampling in multimedia systems. IEEE Trans. on Multimedia, 2006.

[98] Mohan Kankanhalli, Jun Wang, and Ramesh Jain. Experiential sampling on multiple data streams. IEEE Trans. on Multimedia, 2006.

[99] Jun Wang, Johan Pouwelse, Jenneke Fokker, Arjen P. de Vries, and Marcel J.T. Reinders. Personalization on a peer-to-peer television system. Special Issue on Multimedia Tools and Applications, 2006.

[100] Jun Wang, Arjen P. de Vries, and Marcel J.T. Reinders. Probabilistic relevance models for collaborative filtering. In Proc. of the Twelfth annual conference of the Advanced School for Com- puting and Imaging, 2006.

[101] Jun Wang, Arjen P. de Vries, and Marcel J.T. Reinders. On combining user-based and item- based collaborative filtering approaches. In Proc. of the 27th Symposium on INFORMATION THEORY in the BENELUX, 2006.

[102] Jun Wang. Probabilistic relevance models for collaborative filtering. In SIGIR2006 Doctoral Consortium. Best Doctoral Consortium Award, 2006.

[103] Jun Wang, Marcel J. T. Reinders, Reginald L. Lagendijk, and Johan Pouwelse. Self- organizing distributed collaborative filtering. In Proc. of ACM SIGIR, 2005.

[104] Johan Pouwelse, Michiel van Slobbe, Jun Wang, Marcel J. T. Reinders, and Henk Sips. P2P- based PVR recommendation using friends, taste buddies and superpeers. In Proc. of 2005 International Conference on Intelligent User Interfaces (IUI 2005), Workshop on the Next Stage of Recommender Systems Research, 2005.

[105] Jun Wang, Marcel J.T. Reinders, Johan Pouwelse, and Reginald L. Lagendijk. Wi-Fi walk- man: a wireless handhold that shares and recommends music on peer-to-peer networks. In Proc. of Embedded Processors for Multimedia and Communications II, part of the SPIE Symposium on Electronic Imaging 2005., 2005.

[106] J. Wang, M.J.T. Reinders, R.L. Lagendijk, and J. Pouwelse. Distributed collaborative filtering for peer-to-peer file sharing systems. In Proc. of the Eleventh annual conference of the Advanced School for Computing and Imaging, 2005.

[107] J. Wang, R. Achanta, M.S. Kankanhalli, and M.J.T. Reinders. A sensor fusion approach for tracking faces in compressed video. In Proc. of the Eleventh annual conference of the Advanced School for Computing and Imaging, 2005.

[108] W.Q. Yan, J. Wang, and M. S. Kankanhalli. Automatic video logo detection and removal. ACM Multimedia Systems Journal, 2005.

[109] W.Q. Yan, J. Wang, and M. S. Kankanhalli. Analogies based video editing. ACM Multimedia Systems Journal, 2005.

[110] Jun Wang, Marcel J.T. Reinders, Reginald L. Lagendijk, Jasper Lindenberg, and Mohan S. Kankanhalli. Video content representation on tiny devices. In Proc. of IEEE Int. Conf. on Multimedia and Expo (ICME), 2004.

[111] J. Wang and M.S. Kankanhalli. Experiental sampling for multimedia analysis. In Proc. of ACM Multimedia, 2003.

[112] J. Wang, M.S. Kankanhali, W.Q. Yan, and R. Jain. Experiental sampling for video surveil- lance. In Proc. of First ACM SIGMM International Workshop on Video Surveilance, 2003.

[113] J. Wang, W.Q. Yan, M.S. Kankanhalli, R. Jain, and M.J.T.Reinders. Adaptive monitoring for video surveillance. In Proc. of Fourth IEEE Pacific-Rim Conference, 2003.

[114] J. Wang, W.Q. Yan, M.S. Kankanhalli, R. Jain, and M.J.T.Reinders. Experiental sampling for monitoring. In Proc. of ACM SIGMM 2003 Workshop on Experiential Telepresence, 2003.

[115] C. Madhwacharyalu, J. Wang, W. Yan, and M.S. Kankanhalli. An information integration approach to designing digital video album. In Proc. of Fourth IEEE Pacific-Rim Conference on Conference on Multimedia, 2003.

[116] J.Wang,R.Achanta,andM.S.Kankanhalli.Ahierarchicalframeworkforfacetrackingusing state vector fusion for compressed video. In Proc. of International Conference on Acoustics, Speech, and Signal Processing, 2003.

[117] R. Achanta, J. Wang, and M.S. Kankanhalli. A sensor fusion based object tracker for com- pressed video. In Proc. of International Workshop on Advanced Imaging Technology, 2003.

[118] Jun Wang and Marcel J. T. Reinders. Music recommender system for wi-fi walkman. Tech- nical Report ICT-2003-01, Faculty of Electrical Engineering, Mathematics and Computer Sci- ence, Delft University of technology, Sept. 2003.

[119] Jun Wang. Detecting and tracking human faces in compressed domain for content based video indexing. Master’s thesis, School of Computing, National University of Singapore, 2002.

[120] J. Wang, M.S. Kankanhalli, P. Mulhem, and H.H.Abdulredha. Face detection using dct coef- ficients in mpeg video. In International Workshop on Advanced Imaging Technology, 2002