Automatic news recommendations via aggregated profiling

Today, people have only limited, valuable leisure time at their hands which they want to fill in as good as possible according to their own interests, whereas broadcasters want to produce and distribute news items as fast and targeted as possible. These (developing) news stories can be characterised as dynamic, chained, and distributed events in addition to which it is important to aggregate, link, enrich, recommend, and distribute these news event items as targeted as possible to the individual, interested user. In this paper, we show how personalised recommendation and distribution of news events, described using an RDF/OWL representation of the NewsML-G2 standard, can be enabled by automatically categorising and enriching news events metadata via smart indexing and linked open datasets available on the web of data. The recommendations—based on a global, aggregated profile, which also takes into account the (dis)likings of peer friends—are finally fed to the user via a personalised RSS feed. As such, the ultimate goal is to provide an open, user-friendly recommendation platform that harnesses the end-user with a tool to access useful news event information that goes beyond basic information retrieval. At the same time, we provide the (inter)national community with standardised mechanisms to describe/distribute news event and profile information.

[1]  Hsinchun Chen,et al.  A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce , 2007, IEEE Intelligent Systems.

[2]  Itu-T and Iso Iec Jtc.  Advanced video coding for generic audiovisual services , 2010 .

[3]  Robin D. Burke,et al.  Hybrid Web Recommender Systems , 2007, The Adaptive Web.

[4]  Rik Van de Walle,et al.  Automatic news production , 2008 .

[5]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[6]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[7]  Tim O'Reilly,et al.  What is Web 2.0: Design Patterns and Business Models for the Next Generation of Software , 2007 .

[8]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

[9]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[10]  Hsinchun Chen,et al.  A comparison of collaborative-filtering algorithms for ecommerce , 2007 .

[11]  David Maxwell Chickering,et al.  Dependency Networks for Inference, Collaborative Filtering, and Data Visualization , 2000, J. Mach. Learn. Res..

[12]  Luk Overmeire,et al.  Integration Demands on MAM Systems : A Proof of Concept Solution , 2008 .

[13]  Chris Cornelis,et al.  A Fuzzy Relational Approach to Event Recommendation , 2005, IICAI.

[14]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[15]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[16]  Tim Berners-Lee,et al.  Linked Data - The Story So Far , 2009, Int. J. Semantic Web Inf. Syst..

[17]  Dean P. Foster,et al.  Clustering Methods for Collaborative Filtering , 1998, AAAI 1998.

[18]  Rik Van de Walle,et al.  Production and multi-channel distribution of news , 2008, Multimedia Systems.

[19]  Deborah L. McGuinness,et al.  OWL Web ontology language overview , 2004 .

[20]  Raphaël Troncy.  Bringing the IPTC News Architecture into the Semantic Web , 2008, International Semantic Web Conference.

[21]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[22]  Xin Jin,et al.  Semantically Enhanced Collaborative Filtering on the Web , 2003, EWMF.

[23]  Dimitris Plexousakis,et al.  Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences , 2005, iTrust.

[24]  David M. Pennock,et al.  Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments , 2001, UAI.

[25]  Rik Van de Walle,et al.  Automatic metadata enrichment in news production , 2009, 2009 10th Workshop on Image Analysis for Multimedia Interactive Services.

[26]  George Karypis,et al.  Evaluation of Item-Based Top-N Recommendation Algorithms , 2001, CIKM '01.

[27]  Roberto Turrin,et al.  Do Metrics Make Recommender Algorithms? , 2009, 2009 International Conference on Advanced Information Networking and Applications Workshops.

[28]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[29]  Chunyan Miao,et al.  Trust-based agent community for collaborative recommendation , 2006, AAMAS '06.

[30]  John Riedl,et al.  Combining Collaborative Filtering with Personal Agents for Better Recommendations , 1999, AAAI/IAAI.