Browse > Article
http://dx.doi.org/10.3837/tiis.2019.10.001

Kakao Deep Reading Index: Consumption Time as a Key Factor in News Curation Algorithm  

Lee, Dongkwon (Kakao Corp.)
Kim, Daewon (Kakao Corp.)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.10, 2019 , pp. 4833-4848 More about this Journal
Abstract
This paper introduces the structure and effects of Kakao's news curation algorithm, which is created based on the Deep Reading Index (DRI). The DRI examines the extent of deep reading through content reading time, that is, the duration of reader engagement with an article. Current news curation algorithms focus on reader choice, with the click-through rate or pageviews as the gauge for consumption frequency. DRI is a product of the challenge of introducing and adopting a new factor called 'consumption time' instead of 'frequency of consumption', which is the basis of existing curation algorithms. The analysis of DRI-based services proves that the new algorithm can act as a curation system that is more effective in providing in-depth and quality news reports.
Keywords
News curation; Recommendation algorithm; Consumption time; Deep reading index; Kakao;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L. and Riedl, J., "Applying collaborative filtering to Usenet News," Communication of the ACM, 40(3), 77-87, 1997.   DOI
2 Viana, P., & Soares, M., "A hybrid approach for personalized news recommendation in a mobility scenario using long-short user interest," International Journal on Artificial Intelligence Tools, 26(02), 1760012, 2017.   DOI
3 Das, A. S., Datar, M., Garg, A., & Rajaram, S., "Google news personalization: scalable online collaborative filtering," in Proc. of the 16th international conference on World Wide Web, ACM, pp. 271-280, 2007.
4 Liu, J., Dolan, P., & Pedersen, E. R., " Personalized news recommendation based on click behavior," in Proc. of the 15th international conference on Intelligent user interfaces, ACM, pp. 31-40, 2010.
5 Aggarwal, C. C., Wolf, J. L., Wu, K. L., & Yu, P. S., "Horting hatches an egg: A new graph-theoretic approach to collaborative filtering," in Proc. of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp. 201-212, 1999.
6 Chu, W. Park, S. T., Beaupre, T., Motgi, N., Phadke, A., Chakraborty, S., & Zachariah, J., "A case study of behavior-driven conjoint analysis on Yahoo!: front page today module," in Proc. of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp. 1097-1104, 2009.
7 Chu, W. & Park, S. T., "Personalized recommendation on dynamic content using predictive bilinear models," in Proc. of the 18th international conference on World wide web, ACM, pp. 691-700, 2009.
8 Li, L., Chu, W., Langford, J., Schapire, R., "A contextual-bandit approach to personalized news article recommendation," in Proc. of the 19th international conference on World wide web, pp. 661-670, 2010.
9 Park, S., Sung, I., Seo, S., Hwang, J., Noh, J., Kim, D., "News recommendation service using machine learning: focusing on Kakao's RUBICS," Journal of Cybercommunication Academic Society, 34(1), 5-48, 2017.
10 Hirschman, A. O., "Exit, voice, and loyalty: Responses to decline in firms, organizations, and states," The Journal of Finance, 25(5), 1194-1195, 1970.   DOI
11 Van Dijk, W. W., Zeelenberg, M., "What do we talk about when we talk about disappointment? Distinguishing outcome-related disappointment from person-related disappointment," Cognition & Emotion, 16(6), 787-807, 2002.   DOI
12 Li, L., Zheng, L., Yang, F., & Li, T., "Modeling and broadening temporal user interest in personalized news recommendation," Expert Systems with Applications, 41(7), 3168-3177, 2014.   DOI
13 Zhang, F., "A personalized time-sequence-based book recommendation algorithm for digital libraries," IEEE access, 4, 2714-2720, 2016.   DOI