A Personalized and Context-Aware News Offer for Mobile Devices

  • Toon De PessemierEmail author
  • Kris Vanhecke
  • Luc Martens
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 246)


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.


Recommender system Context-aware Real-time Mobile News User evaluation 


  1. 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)CrossRefGoogle Scholar
  2. 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. 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). Scholar
  4. 4.
    Apache Software Foundation: Apache storm (2015).
  5. 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).
  6. 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).
  7. 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). Scholar
  8. 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). Scholar
  9. 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).
  10. 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)CrossRefGoogle Scholar
  11. 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). Scholar
  12. 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). Scholar
  13. 13.
    Elastic: Elasticsearch (2015).
  14. 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. 15.
    Google: Google Hourly Trends (2015).
  16. 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). Scholar
  17. 17.
    Hatcher, E., Gospodnetic, O.: Lucene in action (in action series) (2004)Google Scholar
  18. 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). Scholar
  19. 19.
    Katta: Lucune & more in the cloud (2015).
  20. 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. 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). Scholar
  22. 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). Scholar
  23. 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).
  24. 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). Scholar
  25. 25.
    Manning, C.D., Raghavan, P., Schütze, H., et al.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  26. 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)CrossRefGoogle Scholar
  27. 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).
  28. 28.
    Porter, M.F.: Snowball: a language for stemming algorithms (2001).
  29. 29.
    Reuters Institute for the Study of Journalism: Digital News Report (2014).
  30. 30.
    Ricci, F.: Mobile recommender systems. Inf. Technol. Tourism 12(3), 205–231 (2010)CrossRefGoogle Scholar
  31. 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 2012Google Scholar
  32. 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. 33.
    Shani, G., Gunawardana, A.: Tutorial on application-oriented evaluation of recommendation systems. AI Commun. 26(2), 225–236 (2013)MathSciNetGoogle Scholar
  34. 34.
    Shaphira, B., Rokach, L.: Recommender systems and search engines-two sides of the same coin? Slide Lecture (2012).
  35. 35.
    Telematica Instituut / Novay: Duine Framework (2009).
  36. 36.
    The Apache Software Foundation: Apache Lucene (2015).
  37. 37.
    The Apache Software Foundation: Apache Mahout (2015).
  38. 38.
    The Apache Software Foundation: Apache Solr (2015).
  39. 39.
    Reuters, T.: Open Calais (2008–2013).
  40. 40.
    Weiss, A.S.: Exploring news apps and location-based services on the smartphone. Journalism Mass Commun. Q. 90(3), 435–456 (2013)CrossRefGoogle Scholar
  41. 41.
  42. 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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Toon De Pessemier
    • 1
    Email author
  • Kris Vanhecke
    • 1
  • Luc Martens
    • 1
  1. 1.Department of Information TechnologyiMinds, Ghent UniversityGhentBelgium

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