Stories Around You - A Two-Stage Personalized News Recommendation

Youssef Meguebli, Mouna Kacimi, Bich-liên Doan, Fabrice Popineau

2014

Abstract

With the tremendous growth of published news articles, a key issue is how to help users find diverse and interesting news stories. To this end, it is crucial to understand and build accurate profiles for both users and news articles. In this paper, we define a user profile based on (1) the set of entities she/he talked about it in her/his comments and (2) the set of key-concepts related to those entities on which the user has expressed a viewpoint. The same information is extracted from the content of each news article to create its profile. These profiles are then matched for the purpose of recommendation using a new similarity measure. We use also the news articles profiles to diversify the list of recommended stories. A first evaluation involving the activities of 150 real users in four news web sites, namely The Independent, The Telegraph, CNN and Aljazeera has shown the effectiveness of our approach compared to recent works.

References

  1. Abbar, S., Amer-Yahia, S., Indyk, P., and Mahabadi, S. (2013). Real-time recommendation of diverse related articles. In Proceedings of the 22Nd International Conference on World Wide Web, WWW 7813, pages 1-12, Republic and Canton of Geneva, Switzerland.
  2. Abel, F., Gao, Q., Houben, G.-J., and Tao, K. (2011). Analyzing user modeling on twitter for personalized news recommendations. In Proceedings of the 19th International Conference on User Modeling, Adaption, and Personalization, UMAP'11, pages 1-12, Berlin, Heidelberg. Springer-Verlag.
  3. Agrawal, R., Gollapudi, S., Halverson, A., and Ieong, S. (2009). Diversifying search results. In WSDM, pages 5-14.
  4. Carterette, B. and Chandar, P. (2009). Probabilistic models of ranking novel documents for faceted topic retrieval. In CIKM, pages 1287-1296.
  5. Chen, J., Nairn, R., Nelson, L., Bernstein, M., and Chi, E. (2010). Short and tweet: Experiments on recommending content from information streams. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 7810, pages 1185-1194, New York, NY, USA. ACM.
  6. Clarke, C. L. A., Kolla, M., Cormack, G. V., Vechtomova, O., Ashkan, A., Büttcher, S., and MacKinnon, I. (2008). Novelty and diversity in information retrieval evaluation. In SIGIR, pages 659-666.
  7. Gollapudi, S. and Sharma, A. (2009). An axiomatic approach for result diversification. In Proceedings of the 18th International Conference on World Wide Web, WWW 7809, pages 381-390, New York, NY, USA. ACM.
  8. Hassin, R., Rubinstein, S., and Tamir, A. (1997). Approximation algorithms for maximum dispersion. Operations Research Letters, 21:133-137.
  9. Hong, L. and Davison, B. D. (2010). Empirical study of topic modeling in twitter. In Proceedings of the First Workshop on Social Media Analytics, SOMA 7810, pages 80-88, New York, NY, USA. ACM.
  10. Kacimi, M. and Gamper, J. (2011). Diversifying search results of controversial queries. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 7811, pages 93- 98, New York, NY, USA. ACM.
  11. Kacimi, M. and Gamper, J. (2012). Mouna: Mining opinions to unveil neglected arguments. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM 7812, pages 2722-2724, New York, NY, USA. ACM.
  12. Korte, B. and Hausmann, D. (1978). An analysis of the greedy heuristic for independence systems. Annals of Discrete Mathematics, 2:65-74.
  13. Li, Q., Wang, J., Chen, Y. P., and Lin, Z. (2010). User comments for news recommendation in forum-based social media. Inf. Sci., 180(24):4929-4939.
  14. Meguebli, B.-L. Y., Kacimi, M., Doan, B.-l., and Popineau, F. (2014a). Building rich user profiles for personalMeguebli, Y., Kacimi, M., Doan, B.-L., and Popineau, F. (2014b). Unsupervised approach for identifying users political orientations. In Advances in Information Retrieval, pages 507-512. Springer.
  15. Michelson, M. and Macskassy, S. A. (2010). Discovering users' topics of interest on twitter: A first look. In Proceedings of the Fourth Workshop on Analytics for Noisy Unstructured Text Data, AND 7810, pages 73- 80, New York, NY, USA. ACM.
  16. Phelan, O., McCarthy, K., and Smyth, B. (2009). Using twitter to recommend real-time topical news. In Proceedings of the Third ACM Conference on Recommender Systems, RecSys 7809, pages 385-388, New York, NY, USA. ACM.
  17. Radlinski, F. and Dumais, S. T. (2006). Improving personalized web search using result diversification. In SIGIR, pages 691-692.
  18. Santos, R. L. T., Macdonald, C., and Ounis, I. (2010). Selectively diversifying web search results. In CIKM, pages 1179-1188.
  19. Shmueli, E., Kagian, A., Koren, Y., and Lempel, R. (2012). Care to comment?: Recommendations for commenting on news stories. In Proceedings of the 21st International Conference on World Wide Web, WWW 7812, pages 429-438, New York, NY, USA. ACM.
  20. Stoyanovich, J., Amer-yahia, S., Marlow, C., and Yu, C. (2008). Leveraging tagging to model user interests in del.icio.us. In In AAAI SIP.
  21. Wang, J. and Zhu, J. (2009). Portfolio theory of information retrieval. In SIGIR, pages 115-122.
  22. Weng, J., Lim, E.-P., Jiang, J., and He, Q. (2010). Twitterrank: finding topic-sensitive influential twitterers. In Proceedings of the third ACM international conference on Web search and data mining, WSDM 7810, pages 261-270, New York, NY, USA. ACM.
  23. Zhai, C. and Lafferty, J. D. (2006). A risk minimization framework for information retrieval. Inf. Process. Manage., 42(1):31-55.
Download


Paper Citation


in Harvard Style

Meguebli Y., Kacimi M., Doan B. and Popineau F. (2014). Stories Around You - A Two-Stage Personalized News Recommendation . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 473-479. DOI: 10.5220/0005159804730479


in Bibtex Style

@conference{kdir14,
author={Youssef Meguebli and Mouna Kacimi and Bich-liên Doan and Fabrice Popineau},
title={Stories Around You - A Two-Stage Personalized News Recommendation},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={473-479},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005159804730479},
isbn={978-989-758-048-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - Stories Around You - A Two-Stage Personalized News Recommendation
SN - 978-989-758-048-2
AU - Meguebli Y.
AU - Kacimi M.
AU - Doan B.
AU - Popineau F.
PY - 2014
SP - 473
EP - 479
DO - 10.5220/0005159804730479