News Recommendation Systems in the Era of Information Overload
DOI:
https://doi.org/10.13052/jwe1540-9589.20210Keywords:
Information overload, internet news, recommendation systems, User-CF, Item-CF, reflection on technologyAbstract
The internet has reconstructed information boundaries in the modern world, and along with mobile internet has become the most important source of information for the public. Simultaneously, the internet has brought humanity into an era of information overload. In response to this information overload, recommendation systems backed by big data and smart algorithms have become highly popular on information platforms on the internet. There have already been many studies that attempted to improve and upgrade recommendation algorithms from a technical perspective, but the field lacks a comprehensive reflection on news recommendation algorithms. In our study, we summarize the principles and characteristics of current news recommendation algorithms and discuss “unexpected consequences” that might arise from these algorithms. In particular, technical bottlenecks include cold starts and data sparsity, and moral bottlenecks are presented in the form of information imbalance and manipulation. These problems may cause new recommendation systems to become a “warped mirror”.
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References
Maes P. Agents that reduce work and information overload [J]. Communications of the ACM, 1995, 377:30–40.
Jing Qiu, Lejian Liao, Peng Li. News Recommender System Based on Topic Detection and Tracking [M]. Rough Sets and Knowledge Technology. Springer Berlin Heidelberg, 2009.
Chen W, Zhang LJ, Chen C, Bu JJ. A Hybrid Phonic Web News Recommender System for Pervasive Access [C]. Wri International Conference on Communications & Mobile Computing. IEEE, 2009, pp. 122–126.
Liu Cundi, Xu Wei. Can algorithms define society? News algorithm recommendation system from the perspective of media sociology [J]. Academic Forum, 2018, 41(04):28–37.
Anderson C. The end of theory [J]. Wired Magazine, 2008, 16(7):16–17.
Huang Z, Chen H, Zeng D. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering [J]. ACM Transactions on Information Systems, 2004, (22):116–142.
Breese JS, Heckerman D, Kadie C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering [J]. Uncertainty in Artificial Intelligence, 2013, 98(7):43–52.
Ren J, Zhou T, Zhang YC. Information Filtering via Self-Consistent Refinement [J]. Epl, 2008, 82(5):1–4.
Sun D, Zhou T, Liu JG, et al. Information filtering based on transferring similarity [J]. Physical Review E, 2009, 80(1):17101–17101.
Schein AI, Popescul A, Ungar LH, Pennock DM. Methods and metrics for cold-start recommendations. in: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM Press, New York, 2002, pp. 253–260.
X. N. Lam, T. Vu, T. D. Le, A. D. Duong, Addressing cold-start problem in recommendation systems [C]. in: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, 2008, pp. 208–211.
Zhang ZK, Liu C, Zhang YC, et al. Solving the cold-start problem in recommender systems with social tags [J]. EPL (Europhysics Letters), 2010, 92(2):28002p1–p6.
Chen Chang. Today’s headlines and Zhang Yiming’s eyes on future media: it’s time to give the power of filtering information to social relationships and algorithms [EB/OL]. http://www.cyzone.cn/a/20160115/288570.html,2016-01-15.
Mobasher B, Burke RD, Bhaumik R, et al. Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness [J]. ACM Transactions on Internet Technology (TOIT), 2007, 7(2):1–41.
Sandvig JJ, Mobasher B, Burke R. Robustness of collaborative recommendation based on association rule mining [C]. in: Proceedings of the 2007 ACM Conference on Recommender Systems, ACM Press, 2007, pp. 105–112.
Shyong K, Frankowski D, Riedl J. Do You Trust Your Recommendations? An Exploration of Security and Privacy Issues in Recommender Systems [C]. Springer, Heidelberg, Germany, 2006, pp. 14–29.
Resnick P, Sami R. The influence limiter: provably manipulation-resistant recommender systems [C]. in: Proceedings of the 2007 ACM Conference on Recommender Systems, ACM Press, 2007, pp. 25–32.
Shi C, Kaminsky M, Gibbons PB, Xiao F. DSybil: Optimal Sybil-Resistance for Recommendation Systems [C], IEEE Press, 2009, pp. 283–298.
Tufekci, Z. Algorithmic Harms Beyond Facebook and Google: Emergent Challenges of Computational Agency [J]. Colorado Technology Law Journal, 2015(2):396–412.
Epstein R, Robertson RE. The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections [J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(33):4512–21.
