Abstract
Recommender systems have become an important component of Web media. From VoD providers (Netflix, Amazon Video) (https://goo.gl/g3G1ys) to news websites (Yahoo! News, CNN) (https://goo.gl/yA2NB6), users have become accustomed to personalized content. However, news recommendation differs from traditional recommendation due to the short lifetime of news. Indeed, News is particularly characterized by a short time span during which they are relevant. Therefore, in addition, to suggest suited news to users, news recommender systems (NRS) have to deal with news recency in order to avoid recommending already read content somewhere else.
In most of the cases, NRS implicitly collect users’ click history and readings to build topic-based user profiles. News websites generally integrate some keywords into the news articles which sum up their content. But this is not always the case for African news websites.
In this paper, we present Follow Africa, an African news recommender system. We introduce a recency-based recommendation model which also takes account of users’ previous readings. We show the effectiveness of our proposal through the results we obtain in a month-lasted online experiment with more than one hundred users.
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Notes
- 1.
One can install Follow Africa’s Android mobile application from https://goo.gl/t8nahh.
References
Abel, F., Deldjoo, Y., Elahi, M., Kohlsdorf, D.: Recsys challenge 2017: offline and online evaluation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, pp. 372–373. ACM, New York (2017)
Billsus, D., Pazzani, M.J.: A hybrid user model for news story classification. In: Kay, J. (ed.) UM99 User Modeling. CICMS, vol. 407, pp. 99–108. Springer, Vienna (1999). https://doi.org/10.1007/978-3-7091-2490-1_10
Calandrino, J.A., Kilzer, A., Narayanan, A., Felten, E.W., Shmatikov, V.: “you might also like:” privacy risks of collaborative filtering. In: 2011 IEEE Symposium on Security and Privacy, pp. 231–246, May 2011
Dakka, W., Gravano, L.: Efficient summarization-aware search for online news articles. In: JCDL, pp. 63–72. ACM (2007)
Dridan, R., Oepen, S.: Tokenization: returning to a long solved problem a survey, contrastive experiment, recommendations, and toolkit. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2, ACL 2012, pp. 378–382. Association for Computational Linguistics, Stroudsburg (2012)
Garcin, F., Zhou, K., Faltings, B., Schickel, V.: Personalized news recommendation based on collaborative filtering. In: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01, WI-IAT 2012, pp. 437–441. IEEE Computer Society, Washington (2012)
Kille, B., Hopfgartner, F., Brodt, T., Heintz, T.: The plista dataset. In: Proceedings of the 2013 International News Recommender Systems Workshop and Challenge, NRS 2013, pp. 16–23. ACM, New York (2013)
Kohavi, R., Longbotham, R., Sommerfield, D., Henne, R.: Controlled experiments on the web: survey and practical guide. Data Min. Knowl. Disc. 18(1), 140–181 (2009)
Kompan, M., Bieliková, M.: Content-based news recommendation. In: Buccafurri, F., Semeraro, G. (eds.) EC-Web 2010. LNBIP, vol. 61, pp. 61–72. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15208-5_6
Li, L., Wang, D.-D., Zhu, S.-Z., Li, T.: Personalized news recommendation: a review and an experimental investigation. J. Comput. Sci. Technol. 26(5), 754–766 (2011)
Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, IUI 2010, pp. 31–40. ACM, New York (2010)
Shah, L., Gaudani, H., Balani, P.: Article: survey on recommendation system. Int. J. Comput. Appl. 137(7), 43–49 (2016). Published by Foundation of Computer Science (FCS), NY, USA
Volkovs, M., Yu, G.W., Poutanen, T.: Content-based neighbor models for cold start in recommender systems. In: Proceedings of the Recommender Systems Challenge 2017, RecSys Challenge 2017, pp. 7:1–7:6. ACM, New York (2017)
Wang, Z., Hahn, K., Kim, Y., Song, S., Seo, J.-M.: A news-topic recommender system based on keywords extraction. Multimedia Tools Appl. 77(4), 4339–4353 (2018)
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Fall, M.D., Gueye, M. (2019). Follow Africa: Building an African News Recommender Systems. In: Bassioni, G., Kebe, C., Gueye, A., Ndiaye, A. (eds) Innovations and Interdisciplinary Solutions for Underserved Areas. InterSol 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-34863-2_7
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