Abstract
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.
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Notes
RSS:Really Simple Syndication, also see http://www.rss-specifications.com/
References
Bizer C et al (2009) Linked data—the story so far. Int J Semant Web Inf Syst 5(3):1–22
Breese J, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th conference on uncertainty in artificial intelligence, Madison, USA, pp 43–52
Burke R (2007) The adaptive web, chap. Hybrid web recommender systems. Springer, Berlin, Heidelberg, pp 377–408 http://portal.acm.org/citation.cfm?id=1768197.1768211 http://portal.acm.org/citation.cfm?id=1768197.1768211
Campochiaro E et al (2009) Do metrics make recommender algorithms? In: International conference on advanced information networking and applications workshops, vol 23, pp 648–653. doi:http://doi.ieeecomputersociety.org/10.1109/WAINA.2009.127
Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M (1999) Combining content-based and collaborative filters in an online newspaper. In: Proceedings of ACM SIGIR workshop on recommender systems
Corcoran S (2009) Using social applications in ad campaigns. Available at http://www.forrester.com/Research/Document/Excerpt/0,7211,54050,00.html
Cornelis C et al (1998) Clustering methods for collaborative filtering. In: Proceedings of the 15th national conference on artificial intelligence—workshop on recommendation systems, Madison, USA, pp 114–129
Cornelis C et al (2005) A fuzzy relational approach to event recommendation. In: Proceedings of the 1st Indian international conference on artificial intelligence, Pune, India, pp 2231–2242
De Geyter M et al (2008) File-based broadcast workflows: on MAM systems and their integration demands. SMPTE Motion Imaging J 8(11–12):38–46
De Sutter R et al (2008) Automatic news production. In: Proceedings of the international broadcasting conference, Amsterdam, The Netherlands, pp 158–165
Good N, Schafer JB, Konstan JA, Borchers A, Sarwar B, Herlocker J, Riedl J (1999) Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of the sixteenth national conference on artificial intelligence, pp 439–446
Heckerman D, Chickering DM, Meek C, Rounthwaite R, Kadie C (2001) Dependency networks for inference, collaborative filtering, and data visualization. J Mach Learn Res 1:49–75. doi:10.1162/153244301753344614
Herlocker J et al (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd international ACM SIGIR conference on research and development in information retrieval. Berkeley, USA, pp 230–237
Huang Z et al (2007) A comparison of collaborative-filtering recommendation algorithms for e-commerce. IEEE Int Syst 22(5):68–78. doi:10.1109/MIS.2007.80
International Press Telecommunications Council (2009) NewsML-G2 specification—version 2.2. Available at http://www.iptc.com/std/NewsML-G2/NewsML-G2_2.2.zip
Iskold A (2007) The art, science and business of recommendation engines. Available at http://www.readwriteweb.com/archives/recommendation_engines.php
ITU-T and ISO/IEC (2003) Advanced video coding for generic audiovisual services. ITU-T Rec. H.264 and ISO/IEC 14496-10 AVC
Karypis G (2001) Evaluation of item-based top-N recommendation algorithms. In: Proceedings of the 10th international conference on information and knowledge management, Atlanta, USA, pp 247–254
Linden G et al (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Int Comput 7(1):76–80
Mannens E et al (2008) Production and multi-channel distribution of news. Multimedia Syst 14(6):359–368 (Special Issue on Canonical Processes of Media Production)
Mannens E et al (2009) Automatic information enrichment in news production. In: Proceedings of the 10th international workshop on image analysis for multimedia interactive services, London, United Kingdom, pp 61–64
McGuinness D et al (eds) (2004) OWL web ontology language: overview. W3C Recommendation. World Wide Web Consortium. Available at http://www.w3.org/TR/owl-features/
Mobasher B, Jin X, Zhou Y (2003) Semantically enhanced collaborative filtering on the web. In: Proceedings of the 1st European web mining forum (EWMF2003), pp 57–76. http://www.springerlink.com/content/y8bd5n544j91wc8w
Mooney RJ, Roy L (2000) Content-based book recommending using learning for text categorization. In: Proceedings of the fifth ACM conference on Digital libraries, DL ’00. ACM, New York, NY, USA, pp 195–204. doi:10.1145/336597.336662
O’Reilly T (2005) What is web 2.0—design patterns and business models for the next generation of software. Available at http://oreilly.com/pub/a/web2/archive/what-is-web-20.html?page=1
Papagelis M et al (2005) Alleviating the sparsity problem of collaborative filtering using trust inferences. Lect Notes Comput Sci – Trust Manage 3477:224–239
Pazzani MJ, Billsus D (2007) The adaptive web, chap. Content-based recommendation systems. Springer, Berlin, Heidelberg, pp 325–341. http://portal.acm.org/citation.cfm?id=1768197.1768209
Popescul A, Ungar LH, Pennock DM, Lawrence S (2001) Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In: Proceedings of the 17th conference in uncertainty in artificial intelligence, UAI ’01. Morgan Kaufmann, San Francisco, CA, USA, pp 437–444. http://portal.acm.org/citation.cfm?id=647235.720088
Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40:56–58. doi:10.1145/245108.245121
Sarwar B et al (2000) Analysis of recommendation algorithms for E-commerce. In: Proceedings of the 2nd ACM conference on electronic commerce, Minneapolis, USA, pp 158–167
Sarwar BM, Karypis G, Konstan JA, Riedl JT (2000) Application of dimensionality reduction in recommender system—a case study. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=?doi=10.1.1.38.744
SMPTE (2004) Material exchange format (MXF)—file format specification. SMPTE 377M
Troncy R (2008) Bringing the IPTC news architecture into the semantic web. In: 7th international semantic web conference (ISWC’08), Karlsruhe, Germany, pp 483–498
Ungar L, Foster D (1998) Clustering methods for collaborative filtering. In: Proceedings of the workshop on recommendation systems. AAAI Press, Menlo Park California, pp 114–129 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.4026 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.4026
Weng J et al (2006) Trust-based agent community for collaborative recommendation. In: Proceedings of the 5th international joint conference on autonomous agents and multiagent systems, Hakodate, Japan, pp 1260–1262
Acknowledgements
The research activities as described in this paper were funded by Ghent University, the Interdisciplinary Institute for Broadband Technology (IBBT), the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT), the Fund for Scientific Research-Flanders (FWO-Flanders), and the European Union.
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Mannens, E., Coppens, S., De Pessemier, T. et al. Automatic news recommendations via aggregated profiling. Multimed Tools Appl 63, 407–425 (2013). https://doi.org/10.1007/s11042-011-0844-8
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DOI: https://doi.org/10.1007/s11042-011-0844-8