Personalized click shaping through lagrangian duality for online recommendation

SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2012, Pages 485-494

  Agarwal, Deepak ^a   Chen, Bee Chung ^a   Elango, Pradheep ^b   Wang, Xuanhui ^b  

^a LINKEDIN CORPORATION   (United States)

^b FACEBOOK   (United States)

Author keywords

click shaping; dual plan; personalization

Indexed keywords

CHANGING DYNAMICS; CLICK SHAPING; CLICK-THROUGH RATE; DATA SETS; DUAL PLAN; LAGRANGIAN DUALITY; MULTIPLE OBJECTIVES; ON-LINE CONTENTS; ONLINE USERS; PERSONALIZATIONS; SEGMENT-BASED; USER SEGMENT;

CONSTRAINED OPTIMIZATION; INFORMATION RETRIEVAL;

LAGRANGE MULTIPLIERS;

EID: 84866606991     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2348283.2348350     Document Type: Conference Paper

References (27)

1. D. Agarwal and B.-C. Chen. Regression-based latent factor models. In KDD, 2009.
2. D. Agarwal, B.-C. Chen, and P. Elango. Spatio-temporal models for estimating click-through rate. In WWW, 2009.
3. D. Agarwal, B.-C. Chen, P. Elango, and et al. Online models for content optimization. In NIPS, 2008.
4. D. Agarwal, B.-C. Chen, P. Elango, and X. Wang. Click shaping to optimize multiple objectives. In KDD, 2011.
5. P. Auer, N. Cesa-Bianchi, and P. Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 2002.
6. D. A. Berry and B. Fristedt. Bandit Problems: Sequential Allocation of Experiments. Chapman and Hall, 1985.
7. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
8. G. Buscher, L. van Elst, and A. Dengel. Segment-level display time as implicit feedback: a comparison to eye tracking. In SIGIR, 2009.
9. Y. Chen, P. Berkhin, B. Anderson, and N. R. Devanur. Real-time bidding algorithms for performance-based display ad allocation. In KDD, 2011.
10. A. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In WWW, 2007.
11. R. Fain and J. Pederson. Sponsored search: A brief history. Bulletin of American Society of Information and Technology, 2006.
12. P. V. Hentenryck and R. Bent. Online Stochastic Combinatorial Optimization. The MIT Press, 2006.
13. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In SIGIR, 1999.
14. T. Jambor and J. Wang. Optimizing multiple objectives in collaborative filtering. In RecSys, 2010.
15. Y. Koren. Collaborative filtering with temporal dynamics. In KDD, 2009.
16. J. Langford and T. Zhang. The epoch-greedy algorithm for contextual multi-armed bandits. In NIPS, 2008.
17. L. Li, W. Chu, J. Langford, and R. E. Schapire. A contextual-bandit approach to personalized news article recommendation. In WWW, 2010.
18. L. Li, W. Chu, J. Langford, and X. Wang. Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. In WSDM, 2011.
19. L. Li, D. Wang, T. Li, D. Knox, and B. Padmanabhan. Scene: a scalable two-stage personalized news recommendation system. In SIGIR, 2011.
20. J. Liu, P. Dolan, and E. R. Pedersen. Personalized news recommendation based on click behavior. In IUI, 2010.
21. D. Sculley, R. G. Malkin, S. Basu, and R. J. Bayardo. Predicting bounce rates in sponsored search advertisements. In KDD, 2009.
22. D. H. Stern, R. Herbrich, and T. Graepel. Matchbox: large scale online bayesian recommendations. In WWW, 2009.
23. R. Steuer. Multi-criteria optimization: theory, computation, and application. Wiley, 1986.
24. K. M. Svore, M. N. Volkovs, and C. J. C. Burges. Learning to rank with multiple objective functions. In WWW, 2011.
25. E. Vee, S. Vassilvitski, and J. Shanmugasundaran. Optimal online assignment with forecasts. In EC, 2010.
26. J. Vermorel and M. Mohri. Multi-armed bandit algorithms and empirical evaluation. In ECML, 2005.
27. Y. Zhang, J. P. Callan, and T. P. Minka. Novelty and redundancy detection in adaptive filtering. In SIGIR, 2002.