BibSonomyburst/tag/moviesBibSonomy RSS feed for /tag/movies2012-02-15T05:51:19+01:00http://www.bibsonomy.org/bibtex/2ab3d24359d12a5e1f3604adf8ef7ec9c/mschubermschuber2009-07-21T10:00:28+02:00RecSys doctoral-symposium movies prediction ratings <span class="authorEditorList"><a href="/author/Umyarov">Akhmed Umyarov</a> </span><em>RecSys &#039;08: Proceedings of the 2008 ACM conference on Recommender systems, </em><em>page 327--330. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Tue Jul 21 10:00:28 CEST 2009New York, NY, USARecSys '08: Proceedings of the 2008 ACM conference on Recommender systems327--330Leveraging aggregate ratings for improving predictive performance of recommender systems2008RecSys doctoral-symposium movies prediction ratings One of the key problems in recommender systems is accurate estimation of unknown ratings of individual items for individual users in terms of the previously specified ratings and other characteristics of items and users. In this thesis, we investigate a way of improving estimations of individual ratings using externally provided properties of aggregate ratings for groups of items and users, such as an externally specified average rating of action movies provided by graduate students or externally specified standard deviation of ratings for comedy movies.Leveraging aggregate ratings for improving predictive performance of recommender systemshttp://www.bibsonomy.org/bibtex/2d33ed224ea545a5ae80e2bab2ca41973/lefteris8lefteris82009-06-22T17:28:38+02:00hybrid_systems movies recommender_systems <span class="authorEditorList"><a href="/author/Lekakos">George Lekakos</a>, and <a href="/author/Caravelas">Petros Caravelas</a> </span><em>Multimedia Tools Appl.</em> <em>36(1-2):55--70</em> (<em>2008</em>)Mon Jun 22 17:28:38 CEST 2009Hingham, MA, USAMultimedia Tools Appl.1-255--70A hybrid approach for movie recommendation362008hybrid_systems movies recommender_systems http://www.bibsonomy.org/bibtex/2eede96b3765df0c02ac0fa38951aac1b/kurtjxkurtjx2009-01-26T18:01:54+01:00movies social social_network <span class="authorEditorList"><a href="/author/Weng">C.-Y. Weng</a>, <a href="/author/Chu">W.-T. Chu</a>, and <a href="/author/Wu">J.-L. Wu</a> </span><em>Multimedia, IEEE Transactions on</em> <em>11(2):256-271</em> (<em>February 2009</em>)Mon Jan 26 18:01:54 CET 2009Multimedia, IEEE Transactions onFeb. 2256-271RoleNet: Movie Analysis from the Perspective of Social Networks112009movies social social_network <para> With the idea of social network analysis, we propose a novel way to analyze movie videos from the perspective of social relationships rather than audiovisual features. To appropriately describe role's relationships in movies, we devise a method to quantify relations and construct role's social networks, called RoleNet. Based on RoleNet, we are able to perform semantic analysis that goes beyond conventional feature-based approaches. In this work, social relations between roles are used to be the context information of video scenes, and leading roles and the corresponding communities can be automatically determined. The results of community identification provide new alternatives in media management and browsing. Moreover, by describing video scenes with role's context, social-relation-based story segmentation method is developed to pave a new way for this widely-studied topic. Experimental results show the effectiveness of leading role determination and community identification. We also demonstrate that the social-based story segmentation approach works much better than the conventional tempo-based method. Finally, we give extensive discussions and state that the proposed ideas provide insights into context-based video analysis. </para>http://www.bibsonomy.org/bibtex/2e4cc0d7b14b091f64d1377fb8625f92e/cschenkcschenk2008-06-08T12:59:40+02:00collaborative filtering folksonomies imdb kind-of-tags movies netflix paper read:2008 recommendation semantic tagora tags web web2.0 <span class="authorEditorList"><a href="/author/Szomszor">Martin Szomszor</a>, <a href="/author/Cattuto">Ciro Cattuto</a>, <a href="/author/Alani">Harith Alani</a>, <a href="/author/O&#039;Hara">Kieron O&#039;Hara</a>, <a href="/author/Baldassarri">Andrea Baldassarri</a>, <a href="/author/Loreto">Vittorio Loreto</a>, and <a href="/author/Servedio">Vito D.P. Servedio</a> </span><em>4th European Semantic Web Conference, Bridging the Gap between Semantic Web and Web 2.0, </em>(<em>2007</em>)Sun Jun 08 12:59:40 CEST 20084th European Semantic Web Conference, Bridging the Gap between Semantic Web and Web 2.0Folksonomies, the Semantic Web, and Movie Recommendation 2007collaborative filtering folksonomies imdb kind-of-tags movies netflix paper read:2008 recommendation semantic tagora tags web web2.0 While the Semantic Web has evolved to support the meaningful exchange of heterogeneous data through shared and controlled conceptualisations, Web 2.0 has demonstrated that large-scale community tagging sites can enrich the semantic web with readily accessible and valuable knowledge. In this paper, we investigate the integration of a movies folksonomy with a semantic knowledge base about user-movie rentals. The folksonomy is used to enrich the knowledge base with descriptions and categorisations of movie titles, and user interests and opinions. Using tags harvested from the Internet Movie Database, and movie rating data gathered by Netflix, we perform experiments to investigate the question that folksonomy-generated movie tag-clouds can be used to construct better user profiles that reflect a user’s level of interest in different kinds of movies, and therefore, provide a basis for prediction of their rating for a previously unseen movie.Has an overview of the usage of tags.