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A Personalized and Context-Aware News Offer for Mobile Devices

  • Conference paper
Web Information Systems and Technologies (WEBIST 2015)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 246))

Abstract

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer.

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Notes

  1. 1.

    http://www.iminds.be/en/projects/2014/04/17/stream-store.

  2. 2.

    http://www.openlivinglabs.eu/livinglab/iminds-ilabo.

References

  1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Tutorial on context-aware recommender systems. In: Proceedings of the Second ACM Conference on Recommender Systems (RecSys 2008) (2008)

    Google Scholar 

  3. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, US (2011). http://dx.doi.org/10.1007/978-0-387-85820-3_7

    Chapter  Google Scholar 

  4. Apache Software Foundation: Apache storm (2015). http://storm.apache.org/

  5. Baltrunas, L., Ricci, F.: Context-based splitting of item ratings in collaborative filtering. In: Proceedings of the Third ACM Conference on Recommender Systems. RecSys 2009, NY, USA, pp. 245–248 (2009). http://doi.acm.org/10.1145/1639714.1639759

  6. Bogers, T., van den Bosch, A.: Comparing and evaluating information retrieval algorithms for news recommendation. In: Proceedings of the 2007 ACM Conference on Recommender Systems. RecSys 2007, NY, USA, pp. 141–144 (2007). http://doi.acm.org/10.1145/1297231.1297256

  7. Brown, P.F., deSouza, P.V., Mercer, R.L., Pietra, V.J.D., Lai, J.C.: Class-based n-gram models of natural language. Comput. Linguist. 18(4), 467–479 (1992). http://dl.acm.org/citation.cfm?id=176313.176316

    Google Scholar 

  8. Cantador, I., Bellogín, A., Castells, P.: News@hand: a semantic web approach to recommending news. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 279–283. Springer, Heidelberg (2008). http://dx.doi.org/10.1007/978-3-540-70987-9_34

    Chapter  Google Scholar 

  9. Cutting, D., Pedersen, J.: Optimization for dynamic inverted index maintenance. In: Proceedings of the 13th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 1990, NY, USA, pp. 405–411 (1990). http://doi.acm.org/10.1145/96749.98245

  10. De Pessemier, T., De Moor, K., Joseph, W., De Marez, L., Martens, L.: Quantifying subjective quality evaluations for mobile video watching in a semi-living lab context. IEEE Trans. Broadcast. 58(4), 580–589 (2012)

    Article  Google Scholar 

  11. De Pessemier, T., Coppens, S., Geebelen, K., Vleugels, C., Bannier, S., Mannens, E., Vanhecke, K., Martens, L.: Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform. Multimedia Tools Appl. 58(1), 167–213 (2012). http://dx.doi.org/10.1007/s11042-010-0715-8

    Article  Google Scholar 

  12. De Pessemier, T., Dooms, S., Martens, L.: Context-aware recommendations through context and activity recognition in a mobile environment. Multimedia Tools Appl. 72(3), 2925–2948 (2014). http://dx.doi.org/10.1007/s11042-013-1582-x

    Article  Google Scholar 

  13. Elastic: Elasticsearch (2015). https://www.elastic.co/

  14. Følstad, A.: Living labs for innovation and development of information and communication technology: A literature review. Electron. J. Organ. Virtualness 10, 99–131 (2008)

    Google Scholar 

  15. Google: Google Hourly Trends (2015). http://www.google.com/trends/hottrends/atom/hourly

  16. Han, B.J., Rho, S., Jun, S., Hwang, E.: Music emotion classification and context-based music recommendation. Multimedia Tools Appl. 47(3), 433–460 (2010). http://dx.doi.org/10.1007/s11042-009-0332-6

    Article  Google Scholar 

  17. Hatcher, E., Gospodnetic, O.: Lucene in action (in action series) (2004)

    Google Scholar 

  18. Hopfgartner, F., Kille, B., Lommatzsch, A., Plumbaum, T., Brodt, T., Heintz, T.: Benchmarking news recommendations in a living lab. In: Kanoulas, E., Lupu, M., Clough, P., Sanderson, M., Hall, M., Hanbury, A., Toms, E. (eds.) CLEF 2014. LNCS, vol. 8685, pp. 250–267. Springer, Heidelberg (2014). http://dx.doi.org/10.1007/978-3-319-11382-1_21

    Google Scholar 

  19. Katta: Lucune & more in the cloud (2015). http://katta.sourceforge.net/

  20. Kenteris, M., Gavalas, D., Mpitziopoulos, A.: A mobile tourism recommender system. In: Proceedings of the IEEE Symposium on Computers and Communications. ISCC 2010, pp. 840–845. IEEE Computer Society, Washington, DC (2010)

    Google Scholar 

  21. Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: Grouplens: applying collaborative filtering to usenet news. Commun. ACM 40(3), 77–87 (1997). http://doi.acm.org/10.1145/245108.245126

    Article  Google Scholar 

  22. Lee, H., Kim, J., Park, S.: Understanding collaborative filtering parameters for personalized recommendations in e-commerce. Electron. Commer. Res. 7(3–4), 293–314 (2007). http://dx.doi.org/10.1007/s10660-007-9004-7

    Article  MATH  Google Scholar 

  23. Li, L., Wang, D., Li, T., Knox, D., Padmanabhan, B.: Scene: a scalable two-stage personalized news recommendation system. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2011, NY, USA, pp. 125–134 (2011). http://doi.acm.org/10.1145/2009916.2009937

  24. Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, US (2011). http://dx.doi.org/10.1007/978-0-387-85820-3_3

    Chapter  Google Scholar 

  25. Manning, C.D., Raghavan, P., Schütze, H., et al.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  26. Papagelis, M., Plexousakis, D., Kutsuras, T.: Alleviating the sparsity problem of collaborative filtering using trust inferences. In: Herrmann, P., Issarny, V., Shiu, S.C.K. (eds.) iTrust 2005. LNCS, vol. 3477, pp. 224–239. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  27. Phelan, O., McCarthy, K., Smyth, B.: Using twitter to recommend real-time topical news. In: Proceedings of the Third ACM Conference on Recommender Systems. RecSys 2009, NY, USA, pp. 385–388 (2009). http://doi.acm.org/10.1145/1639714.1639794

  28. Porter, M.F.: Snowball: a language for stemming algorithms (2001). http://snowball.tartarus.org/

  29. Reuters Institute for the Study of Journalism: Digital News Report (2014). http://www.digitalnewsreport.org/

  30. Ricci, F.: Mobile recommender systems. Inf. Technol. Tourism 12(3), 205–231 (2010)

    Article  Google Scholar 

  31. Ricci, F.: Contextualizing recommendations. In: ACM RecSys Workshop on Context-Aware Recommender Systems (CARS 2012). In: Conjunction with the 6th ACM Conference on Recommender Systems (RecSys 2012). ACM, September 2012

    Google Scholar 

  32. Said, A., Bellogín, A., de Vries, A.: News recommendation in the wild: Cwi’s recommendation algorithms in the NRS challenge. In: Proceedings of the 2013 International News Recommender Systems Workshop and Challenge. NRS, vol. 13 (2013)

    Google Scholar 

  33. Shani, G., Gunawardana, A.: Tutorial on application-oriented evaluation of recommendation systems. AI Commun. 26(2), 225–236 (2013)

    MathSciNet  Google Scholar 

  34. Shaphira, B., Rokach, L.: Recommender systems and search engines-two sides of the same coin? Slide Lecture (2012). http://medlib.tau.ac.il/teldan-2010/bracha.ppt

  35. Telematica Instituut / Novay: Duine Framework (2009). http://duineframework.org/

  36. The Apache Software Foundation: Apache Lucene (2015). https://lucene.apache.org/

  37. The Apache Software Foundation: Apache Mahout (2015). http://mahout.apache.org/users/recommender/recommender-documentation.html

  38. The Apache Software Foundation: Apache Solr (2015). http://lucene.apache.org/solr/

  39. Reuters, T.: Open Calais (2008–2013). http://www.opencalais.com/

  40. Weiss, A.S.: Exploring news apps and location-based services on the smartphone. Journalism Mass Commun. Q. 90(3), 435–456 (2013)

    Article  Google Scholar 

  41. Woodman, M.: Rome (2015). https://rometools.jira.com/wiki/display/ROME/Home

  42. Yu, Z., Zhou, X., Zhang, D., Chin, C.Y., Wang, X., men, J.: Supporting context-aware media recommendations for smart phones. IEEE Pervasive Comput. 5(3), 68–75 (2006)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, iMinds, Ghent University, G. Crommenlaan 8/201, 9050, Ghent, Belgium

    Toon De Pessemier, Kris Vanhecke & Luc Martens

Authors
  1. Toon De Pessemier
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  2. Kris Vanhecke
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  3. Luc Martens
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Corresponding author

Correspondence to Toon De Pessemier .

Editor information

Editors and Affiliations

  1. University of Paris, Paris, France

    Valérie Monfort

  2. RWTH Aachen University, Aachen, Germany

    Karl-Heinz Krempels

  3. Department of Information Systems, University of Agder, Kristiansand, Vest-Agder Fylke, Norway

    Tim A. Majchrzak

  4. Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia

    Žiga Turk

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De Pessemier, T., Vanhecke, K., Martens, L. (2016). A Personalized and Context-Aware News Offer for Mobile Devices. In: Monfort, V., Krempels, KH., Majchrzak, T.A., Turk, Ž. (eds) Web Information Systems and Technologies. WEBIST 2015. Lecture Notes in Business Information Processing, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-30996-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-30996-5_8

  • Publisher Name: Springer, Cham

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