Concept decompositions for large sparse text data using clustering
Machine Learning
Data Mining for Scientific and Engineering Applications
Data Mining for Scientific and Engineering Applications
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Convex Optimization
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Proceedings of the 25th international conference on Machine learning
Spatio-temporal models for estimating click-through rate
Proceedings of the 18th international conference on World wide web
A case study of behavior-driven conjoint analysis on Yahoo!: front page today module
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting bounce rates in sponsored search advertisements
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Segment-level display time as implicit feedback: a comparison to eye tracking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Explore/Exploit Schemes for Web Content Optimization
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Optimal online assignment with forecasts
Proceedings of the 11th ACM conference on Electronic commerce
Understanding web browsing behaviors through Weibull analysis of dwell time
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Optimizing multiple objectives in collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms
Proceedings of the fourth ACM international conference on Web search and data mining
Learning to rank with multiple objective functions
Proceedings of the 20th international conference on World wide web
Multi-armed bandit algorithms and empirical evaluation
ECML'05 Proceedings of the 16th European conference on Machine Learning
Joint relevance and freshness learning from clickthroughs for news search
Proceedings of the 21st international conference on World Wide Web
Traffic shaping to optimize ad delivery
Proceedings of the 13th ACM Conference on Electronic Commerce
Personalized click shaping through lagrangian duality for online recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Multiple objective optimization in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Learning to translate with multiple objectives
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Content recommendation on web portals
Communications of the ACM
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Recommending interesting content to engage users is important for web portals (e.g. AOL, MSN, Yahoo!, and many others). Existing approaches typically recommend articles to optimize for a single objective, i.e., number of clicks. However a click is only the starting point of a user's journey and subsequent downstream utilities such as time-spent and revenue are important. In this paper, we call the problem of recommending links to jointly optimize for clicks and post-click downstream utilities click shaping. We propose a multi-objective programming approach in which multiple objectives are modeled in a constrained optimization framework. Such a formulation can naturally incorporate various application-driven requirements. We study several variants that model different requirements as constraints and discuss some of the subtleties involved. We conduct our experiments on a large dataset from a real system by using a newly proposed unbiased evaluation methodology [17]. Through extensive experiments we quantify the tradeoff between different objectives under various constraints. Our experimental results show interesting characteristics of different formulations and our findings may provide valuable guidance to the design of recommendation engines for web portals.