Community based feedback techniques to improve video search

In this paper, we present a novel approach to aid users in the difficult task of video search. We use a graph based model based on implicit feedback mined from the interactions of previous users of our video search system to provide recommendations to aid users in their search tasks. This approach means that users are not burdened with providing explicit feedback, while still getting the benefits of recommendations. The goal of this approach is to improve the quality of the results that users find, and in doing so also help users to explore a large and difficult information space. In particular we wish to make the challenging task of video search much easier for users. The results of our evaluation indicate that we achieved our goals, the performance of the users in retrieving relevant videos improved, and users were able to explore the collection to a greater extent.

[1]  Alan F. Smeaton,et al.  TRECVID 2004 Experiments in Dublin City University , 2004, TRECVID.

[2]  Alistair Moffat,et al.  Exploring the similarity space , 1998, SIGF.

[3]  John R. Smith,et al.  Large-scale concept ontology for multimedia , 2006, IEEE MultiMedia.

[4]  C. J. van Rijsbergen,et al.  Report on the need for and provision of an 'ideal' information retrieval test collection , 1975 .

[5]  Tao Mei,et al.  VideoReach: an online video recommendation system , 2007, SIGIR.

[6]  R. Likert “Technique for the Measurement of Attitudes, A” , 2022, The SAGE Encyclopedia of Research Design.

[7]  Martin Halvey,et al.  Analysis of online video search and sharing , 2007, HT '07.

[8]  Marcel Worring,et al.  A Learned Lexicon-Driven Paradigm for Interactive Video Retrieval , 2007, IEEE Transactions on Multimedia.

[9]  Ryen W. White,et al.  Studying the use of popular destinations to enhance web search interaction , 2007, SIGIR.

[10]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[11]  Frank Bentley,et al.  Personal vs. commercial content: the similarities between consumer use of photos and music , 2006, CHI.

[12]  R. Woodworth Archives of psychology , 2010 .

[13]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[14]  Amanda Spink,et al.  From Highly Relevant to Not Relevant: Examining Different Regions of Relevance , 1998, Inf. Process. Manag..

[15]  Wei-Ying Ma,et al.  Multimedia information retrieval: what is it, and why isn't anyone using it? , 2005, MIR '05.

[16]  Nick Craswell,et al.  Random walks on the click graph , 2007, SIGIR.

[17]  João Magalhães,et al.  Video Retrieval Using Search and Browsing , 2004, TRECVID.

[18]  Pattie Maes,et al.  Footprints: history-rich tools for information foraging , 1999, CHI '99.

[19]  Michael G. Christel Establishing the utility of non-text search for news video retrieval with real world users , 2007, ACM Multimedia.

[20]  Michael G. Christel,et al.  Mining Novice User Activity with TRECVID Interactive Retrieval Tasks , 2006, CIVR.

[21]  Tao Mei,et al.  Online video recommendation based on multimodal fusion and relevance feedback , 2007, CIVR '07.

[22]  Bernardo A. Huberman,et al.  Usage patterns of collaborative tagging systems , 2006, J. Inf. Sci..

[23]  Stephen E. Robertson,et al.  Okapi at TREC-3 , 1994, TREC.

[24]  Paul Over,et al.  Evaluation campaigns and TRECVid , 2006, MIR '06.

[25]  Stephen E. Robertson,et al.  GatfordCentre for Interactive Systems ResearchDepartment of Information , 1996 .

[26]  Barry Smyth,et al.  Collecting community wisdom: integrating social search & social navigation , 2007, IUI '07.

[27]  John Adcock,et al.  FXPAL Interactive Search Experiments for TRECVID 2007 , 2007, TRECVID.

[28]  Abigail Sellen,et al.  Understanding photowork , 2006, CHI.

[29]  Frank Hopfgartner,et al.  Simulated Testing of an Adaptive Multimedia Information Retrieval System , 2007, 2007 International Workshop on Content-Based Multimedia Indexing.

[30]  Marcel Worring,et al.  Learned Lexicon-Driven Interactive Video Retrieval , 2006, CIVR.

[31]  Chih-Wei Huang Automatic Closed Caption Alignment Based on Speech Recognition Transcripts , 2003 .

[32]  Shih-Fu Chang,et al.  Combining text and audio-visual features in video indexing , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[33]  Abigail Sellen,et al.  Understanding videowork , 2007, CHI.

[34]  Diane Kelly,et al.  Implicit feedback for inferring user preference , 2003 .

[35]  Gerard Salton,et al.  Improving retrieval performance by relevance feedback , 1997, J. Am. Soc. Inf. Sci..

[36]  Joemon M. Jose,et al.  Glasgow University at TRECVid 2006 , 2006, TRECVID.

[37]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[38]  K. Sparck Jones,et al.  INFORMATION RETRIEVAL TEST COLLECTIONS , 1976 .

[39]  Micheline Hancock-Beaulieu,et al.  An Evaluation of Automatic Query Expansion in an Online Library Catalogue , 1992, J. Documentation.

[40]  Jaime Teevan,et al.  Implicit feedback for inferring user preference: a bibliography , 2003, SIGF.

[41]  Barry Smyth,et al.  Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine , 2004, User Modeling and User-Adapted Interaction.