The Information Value of Context for a Mobile News Service

Toon De Pessemier, Kris Vanhecke, Luc Martens


Traditional recommender systems provide personal suggestions based on the user’s preferences, without taking into account any additional contextual information such as time or device type. However, in many applications, this contextual information may be relevant for the human decision process, and as a result, be important to incorporate into the recommendation process, which gave rise to context-aware recommender systems. However, the information value of contextual data for the recommendation process is highly dependent on the application domain and the users’ consumption behavior in different contextual situations. This research aims to assess the information value of context for a recommender system of a mobile news service by analyzing user interactions and feedback. A large-scale user study shows that context-aware recommendations outperform traditional recommendations, but also indicates that the accuracy improvement might be limited in a real-life situation. Service usage takes place in a limited number of different contexts due to user habits and repetitive behavior, leaving little room for optimization based on the context. Data fragmentation over different contextual situations strengthens the sparsity problem, thereby limiting the user-perceived accuracy gain obtained by incorporating context in the recommender. These findings are important for news providers when considering to offer context-aware recommendations.


  1. Adomavicius, G. and Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734-749.
  2. Adomavicius, G. and Tuzhilin, A. (2008). Tutorial on context-aware recommender systems. In Proceedings of the second ACM conference on Recommender Systems (RecSys 7808).
  3. Adomavicius, G. and Tuzhilin, A. (2011). Context-aware recommender systems. In Ricci, F., Rokach, L., Shapira, B., and Kantor, P. B., editors, Recommender Systems Handbook, pages 217-253. Springer US.
  4. Baltrunas, L. and Ricci, F. (2009). Context-based splitting of item ratings in collaborative filtering. In Proceedings of the Third ACM Conference on Recommender Systems, RecSys 7809, pages 245-248, New York, NY, USA. ACM.
  5. Cantador, I., Bellogín, A., and Castells, P. (2008). News@hand: A semantic web approach to recommending news. In Nejdl, W., Kay, J., Pu, P., and Herder, E., editors, Adaptive Hypermedia and Adaptive Web-Based Systems, volume 5149 of Lecture Notes in Computer Science, pages 279-283. Springer Berlin Heidelberg.
  6. De Pessemier, T., Dooms, S., and Martens, L. (2014). Context-aware recommendations through context and activity recognition in a mobile environment. Multimedia Tools and Applications, 72(3):2925-2948.
  7. Følstad, A. (2008). Living labs for innovation and development of information and communication technology: A literature review. Electronic Journal of Organizational Virtualness, 10:99-131.
  8. Han, B.-J., Rho, S., Jun, S., and Hwang, E. (2010). Music emotion classification and context-based music recommendation. Multimedia Tools Appl., 47(3):433- 460.
  9. Hopfgartner, F., Kille, B., Lommatzsch, A., Plumbaum, T., Brodt, T., and Heintz, T. (2014). Benchmarking news recommendations in a living lab. In Kanoulas, E., Lupu, M., Clough, P., Sanderson, M., Hall, M., Hanbury, A., and Toms, E., editors, Information Access Evaluation. Multilinguality, Multimodality, and Interaction, volume 8685 of Lecture Notes in Computer Science, pages 250-267. Springer International Publishing.
  10. Kenteris, M., Gavalas, D., and Mpitziopoulos, A. (2010). A mobile tourism recommender system. In Proceedings of the The IEEE symposium on Computers and Communications, ISCC 7810, pages 840-845, Washington, DC, USA. IEEE Computer Society.
  11. Lee, H., Kim, J., and Park, S. (2007). Understanding collaborative filtering parameters for personalized recommendations in e-commerce. Electronic Commerce Research, 7(3-4):293-314.
  12. Li, L., Wang, D., Li, T., Knox, D., and Padmanabhan, B. (2011). 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 7811, pages 125-134, New York, NY, USA. ACM.
  13. Papagelis, M., Plexousakis, D., and Kutsuras, T. (2005). Alleviating the sparsity problem of collaborative filtering using trust inferences. In Herrmann, P., Issarny, V., and Shiu, S., editors, Trust Management, volume 3477 of Lecture Notes in Computer Science, pages 224-239. Springer Berlin Heidelberg.
  14. Reuters Institute for the Study of Journalism (2014). Digital News Report. Available at
  15. Ricci, F. (2010). Mobile recommender systems. Information Technology & Tourism, 12(3):205-231.
  16. Ricci, F. (2012). Contextualizing recommendations. In ACM RecSys Workshop on Context-Aware Recommender Systems (CARS 7812), In conjunction with the 6th ACM Conference on Recommender Systems (RecSys 7812). ACM.
  17. Said, A., Bellogín, A., and de Vries, A. (2013). News recommendation in the wild: Cwis recommendation algorithms in the nrs challenge. In Proceedings of the 2013 International News Recommender Systems Workshop and Challenge. NRS, volume 13.
  18. Shani, G. and Gunawardana, A. (2013). Tutorial on application-oriented evaluation of recommendation systems. AI Communications, 26(2):225-236.
  19. Telematica Instituut / Novay (2009). Duine Framework. Available at
  20. Thomson Reuters (2008-2013). Open Calais. Available at
  21. Weiss, A. S. (2013). Exploring news apps and locationbased services on the smartphone. Journalism & Mass Communication Quarterly, 90(3):435-456.
  22. Yu, Z., Zhou, X., Zhang, D., Chin, C.-Y., Wang, X., and men, J. (2006). Supporting context-aware media recommendations for smart phones. Pervasive Computing, IEEE, 5(3):68-75.

Paper Citation

in Harvard Style

De Pessemier T., Vanhecke K. and Martens L. (2015). The Information Value of Context for a Mobile News Service . In Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-106-9, pages 363-369. DOI: 10.5220/0005408603630369

in Bibtex Style

author={Toon De Pessemier and Kris Vanhecke and Luc Martens},
title={The Information Value of Context for a Mobile News Service},
booktitle={Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},

in EndNote Style

JO - Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - The Information Value of Context for a Mobile News Service
SN - 978-989-758-106-9
AU - De Pessemier T.
AU - Vanhecke K.
AU - Martens L.
PY - 2015
SP - 363
EP - 369
DO - 10.5220/0005408603630369