Jinyun Yan
ABSTRACT
Social networks and content publishing platforms have newsfeed applications, which show both organic content to drive engagement, and ads to drive revenue. This paper focuses on the problem of ads allocation in a newsfeed to achieve an optimal balance of revenue and engagement. To the best of our knowledge, we are the first to report practical solutions to this business-critical and popular problem in industry.
The paper describes how large-scale recommender system like feed ranking works, and why it is useful to consider ads allocation as a post-operation once the ranking of organic items and (separately) the ranking of ads are done. A set of computationally lightweight algorithms are proposed based on various sets of assumptions in the context of ads on the LinkedIn newsfeed. Through both offline simulation and online A/B tests, benefits of the proposed solutions are demonstrated. The best performing algorithm is currently fully deployed on the LinkedIn newsfeed and is serving all live traffic.
- Gediminas Adomavicius, Nikos Manouselis, and YoungOk Kwon. 2011. Multi-criteria recommender systems. In Recommender systems handbook. Springer, 769--803.Google Scholar
- Deepak Agarwal, Shaunak Chatterjee, Yang Yang, and Liang Zhang. 2015a. Constrained optimization for homepage relevance. In Proceedings of the 24th International Conference on World Wide Web. ACM, 375--384.Google ScholarDigital Library
- Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, and Xuanhui Wang. 2012. Personalized Click Shaping Through Lagrangian Duality for Online Recommendation. In SIGIR (Portland, Oregon, USA). ACM, New York, NY, USA, 485--494.Google Scholar
- Deepak Agarwal, Bee-Chung Chen, Rupesh Gupta, Joshua Hartman, Qi He, Anand Iyer, Sumanth Kolar, Yiming Ma, Pannagadatta Shivaswamy, Ajit Singh, et almbox. 2014a. Activity ranking in LinkedIn feed. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 1603--1612.Google ScholarDigital Library
- Deepak Agarwal, Bee-Chung Chen, Qi He, Zhenhao Hua, Guy Lebanon, Yiming Ma, Pannagadatta Shivaswamy, Hsiao-Ping Tseng, Jaewon Yang, and Liang Zhang. 2015b. Personalizing linkedin feed. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1651--1660.Google ScholarDigital Library
- Deepak Agarwal, Bo Long, Jonathan Traupman, Doris Xin, and Liang Zhang. 2014b. Laser: A scalable response prediction platform for online advertising. In Proceedings of the 7th ACM international conference on Web search and data mining. 173--182.Google ScholarDigital Library
- Jaime Arguello, Fernando Diaz, Jamie Callan, and Ben Carterette. 2011. A methodology for evaluating aggregated search results. In European Conference on Information Retrieval. Springer, 141--152.Google ScholarDigital Library
- Stephen Boyd and Lieven Vandenberghe. 2004. Convex optimization. Cambridge university press.Google ScholarDigital Library
- Christopher Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Gregory N Hullender. 2005. Learning to rank using gradient descent. In Proceedings of the 22nd International Conference on Machine learning (ICML-05). 89--96.Google ScholarDigital Library
- Yair Censor. 1977. Pareto optimality in multiobjective problems. Applied Mathematics and Optimization, Vol. 4, 1 (1977), 41--59.Google ScholarDigital Library
- Ye Chen, Pavel Berkhin, Bo Anderson, and Nikhil R Devanur. 2011. Real-time bidding algorithms for performance-based display ad allocation. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1307--1315.Google ScholarDigital Library
- Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein. 2009. Introduction to algorithms. MIT press.Google ScholarDigital Library
- Benjamin Edelman, Michael Ostrovsky, and Michael Schwarz. 2007. Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American economic review, Vol. 97, 1 (2007), 242--259.Google Scholar
- Yan Gao, Viral Gupta, Jinyun Yan, Changji Shi, Zhongen Tao, PJ Xiao, Curtis Wang, Shipeng Yu, Romer Rosales, Ajith Muralidharan, and Shaunak Chatterjee. 2018. Near Real-time Optimization of Activity-based Notifications. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 283--292.Google ScholarDigital Library
- Mounia Lalmas. 2011. Aggregated search. In Advanced Topics in Information Retrieval. Springer, 109--123.Google Scholar
- Nathan Mantel and William Haenszel. 1959. Statistical aspects of the analysis of data from retrospective studies of disease. Journal of the National Cancer Institute, Vol. 22, 4 (1959), 719--748.Google Scholar
- Mario Rodriguez, Christian Posse, and Ethan Zhang. 2012. Multiple objective optimization in recommender systems. In Proceedings of the sixth ACM conference on Recommender systems. ACM, 11--18.Google ScholarDigital Library
- Shanu Sushmita, Hideo Joho, Mounia Lalmas, and Robert Villa. 2010. Factors affecting click-through behavior in aggregated search interfaces. In Proceedings of the 19th ACM international conference on Information and knowledge management. 519--528.Google ScholarDigital Library
- Krysta M Svore, Maksims N Volkovs, and Christopher JC Burges. 2011. Learning to rank with multiple objective functions. In Proceedings of the 20th international conference on World wide web. ACM, 367--376.Google ScholarDigital Library
- Liang Tang, Bo Long, Bee-Chung Chen, and Deepak Agarwal. 2016. An empirical study on recommendation with multiple types of feedback. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 283--292.Google ScholarDigital Library
- William R. Thompson. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, Vol. 47 (1933), 285--294.Google ScholarCross Ref
- Hal R Varian and Christopher Harris. 2014. The VCG auction in theory and practice. American Economic Review, Vol. 104, 5 (2014), 442--45.Google ScholarCross Ref
- Bo Wang, Zhaonan Li, Jie Tang, Kuo Zhang, Songcan Chen, and Liyun Ru. 2011. Learning to advertise: how many ads are enough?. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 506--518.Google ScholarCross Ref
- Bianca Zadrozny and Charles Elkan. 2002. Transforming classifier scores into accurate multiclass probability estimates. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 694--699.Google ScholarDigital Library
- XianXing Zhang, Yitong Zhou, Yiming Ma, Bee-Chung Chen, Liang Zhang, and Deepak Agarwal. 2016. Glmix: Generalized linear mixed models for large-scale response prediction. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 363--372.Google ScholarDigital Library
- Bo Zhao, Koichiro Narita, Burkay Orten, and John Egan. 2018. Notification Volume Control and Optimization System at Pinterest. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1012--1020.Google ScholarDigital Library
Index Terms
- Ads Allocation in Feed via Constrained Optimization
Recommendations
Ads by whom? ads about what?: exploring user influence and contents in social advertising
COSN '13: Proceedings of the first ACM conference on Online social networksDespite the growing interest in using online social networking services (OSNS) for advertising, little is understood about what contributes to the social advertising performance. In this research, we pose following questions: How many clicks do social ...
Sequential selection of correlated ads by POMDPs
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementOnline advertising has become a key source of revenue for both web search engines and online publishers. For them, the ability of allocating right ads to right webpages is critical because any mismatched ads would not only harm web users' satisfactions ...
Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge ManagementIn the online advertising industry, the process of designing an ad creative i.e., ad text and image) requires manual labor. Typically, each advertiser launches multiple creatives via online A/B tests to infer effective creatives for the target audience, ...
Comments