Implicit User Profiling in News Recommender Systems

Jon Atle Gulla, Arne Dag Fidjestøl, Xiaomeng Su, Humberto Castejon

2014

Abstract

User profiling is an important part of content-based and hybrid recommender systems. These profiles model users’ interests and preferences and are used to assess an item’s relevance to a particular user. In the news domain it is difficult to extract explicit signals from the users about their interests, and user profiling depends on in-depth analyses of users’ reading habits. This is a challenging task, as news articles have short life spans, are unstructured, and make use of unclear and rapidly changing terminologies. This paper discusses an approach for constructing detailed user profiles on the basis of detailed observations of users’ interaction with a mobile news app. The profiles address both news categories and news entities, distinguish between long-term interests and running context, and are currently used in a real iOS mobile news recommender system that recommends news from 89 Norwegian newspapers.

References

  1. Billsus, D. and Pazzani,M. J., 2000. User Modeling for Adaptive News Access. User Modeling and UserAdapted Interaction, 10, pp. 147-180.
  2. Borges, H. L. and Lorena, A. C., 2010. A Survey of Recommender Systems for News Data. In Szczerbicki & Nguyen (eds.), Smart Information and Knowledge Management, SCI 260, pp. 129-151. Springer.
  3. Brasethvik, T. and Gulla, J. A., 2002. A conceptual modeling approach to semantic document retrieval. In Proceedings of the 14th international Conference on Advanced Information Systems Engineering (CAiSE'02), pp. 167-182. Springer.
  4. Cantador, I. Bellogin, A. and Castells, P., 2008. Ontology-Based Personalised and Context-Aware Recommendations of News Items. In Proceedings of the 7th International Conference on Web Intelligence, pp. 562-565. IEEE.
  5. Das, A. S. Datar, M. Garg, A. and Rajaram, S., 2007. Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web, pp. 271- 280. ACM.
  6. Gauch, S., Speretta, M., Chandramouli, A., and Micarelli, A., 2007. User profiles for personalized information access. The adaptive web, pp. 54-89. Springer.
  7. Gulla, J. A. Auran, P. G. and Risvik, K. M., 2002. Linguistic Techniques in Large-Scale Search Engines. In Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems (NLDB'02), pp. 218-222.
  8. Gulla, J. A., Ingvaldsen, J. E., Fidjestøl, A. D., Nilsen, J. E., Haugen, K. R., Su, X., 2014. Learning User Profiles in Mobile News Recommendation. Accepted for publication in Journal of Print and Media Technology Research.
  9. Haugen, K. R., 2013. Mobile News: Design, User Experience and Recommendation. MSc thesis. NTNU, Trondheim.
  10. Jannach, D. Zanker, M. Felfernig, A. and Friedrich, G., 2010. Recommender Systems: An Introduction. Cambridge University Press.
  11. Kim, H. R. and Chan, P. K., 2003. Learning implicit user interest hierarchy for context in personalization. In Proceedings of the 8th international conference on Intelligent user interfaces, pp. 101-108. ACM.
  12. Li, L., Wang, D., Li, T., Know, D., and Padmanabhan, B., 2011. SCENE: a scalable two-stage personalized news recommendation system. In Proceedings of SIGIR'11, pp. 125-134. ACM.
  13. Liu, J. Dolan, P. and Pedersen, E. R., 2010. Personalized news recommendation based on click behavior. In Proceedings of the 15th international conference on intelligent user interfaces, pp. 31-40. ACM.
  14. Lops, P., de Gemmis, M. and Semeraro, G., 2011. Content-based Recommender Systems: State of the Art and Trends. In Ricci, Rokach, Shapira and Kantor (Eds.), Recommender Systems Handbook, Chapter 3, pp. 73-106. Springer.
  15. Nilsen, J. E., 2013. Large-Scale User Click Analysis in News Recommendation. MSc thesis, NTNU, Trondheim.
  16. O'Banion, S. Birnbaum, L. and Hammond, K., 2012. Social media-driven news personalization. In Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web. pp. 45-52. ACM.
  17. Rajaraman, A. and Ullman, J. D., 2011. Mining of Massive Datasets. Cambridge University Press.
  18. Singh, S., Shepherd, M., Duffy, J. and Watters, C., 2006. An Adaptive User Profile for Filtering News Based on a User Interest Hierarchy. In Proceedings of the American Society for Information Science and Technology, Volume 43, Issue 1, pp. 1-21, 2006.
  19. Solskinnsbakk, G. and Gulla, J. A., 2010. Combining ontological profiles with context in information retrieval. Data & Knowledge Engineering, 69(3), pp. 251-260.
  20. Tavakolifard, M. Gulla, J. A. Almeroth, K. C. Ingvaldsen, J. E. Nygreen, G. and Berg, E., 2013. Tailored News in the Palm of your HAND: A Multi-Perspective Transparent Approach to News Recommendation. In Proceedings of 22nd International World Wide Web Conference (WWW'13), Companion Volutme, pp. 305- 308, May, Rio de Janeiro.
Download


Paper Citation


in Harvard Style

Gulla J., Fidjestøl A., Su X. and Castejon H. (2014). Implicit User Profiling in News Recommender Systems . In Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-023-9, pages 185-192. DOI: 10.5220/0004860801850192


in Bibtex Style

@conference{webist14,
author={Jon Atle Gulla and Arne Dag Fidjestøl and Xiaomeng Su and Humberto Castejon},
title={Implicit User Profiling in News Recommender Systems},
booktitle={Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2014},
pages={185-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004860801850192},
isbn={978-989-758-023-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Implicit User Profiling in News Recommender Systems
SN - 978-989-758-023-9
AU - Gulla J.
AU - Fidjestøl A.
AU - Su X.
AU - Castejon H.
PY - 2014
SP - 185
EP - 192
DO - 10.5220/0004860801850192