Learning diverse rankings with multi-armed bandits
Proceedings of the 25th international conference on Machine learning
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
The Journal of Machine Learning Research
Fast active exploration for link-based preference learning using Gaussian processes
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Daily deals: prediction, social diffusion, and reputational ramifications
Proceedings of the fifth ACM international conference on Web search and data mining
Daily-deal selection for revenue maximization
Proceedings of the 21st ACM international conference on Information and knowledge management
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Daily deals sites (DDSs), such as Groupon and LivingSocial, attract millions of customers in the hunt for products and services at significantly reduced prices. A typical approach to increase revenue is to send email messages featuring the deals of the day. Such daily messages, however, are usually not centered on the customers, instead, all registered users typically receive similar messages with almost the same deals. Traditional recommendation algorithms are innocuous in DDSs because: (i) most of the users are sporadic bargain hunters, and thus past preference data is extremely sparse, (ii) deals have a short living period, and thus data is extremely volatile, and (iii) user taste and interest may undergo temporal drifts. In order to address such particularly challenging scenario, we propose new algorithms for daily deals recommendation based on the explore-then-exploit strategy.Users are split into exploration and exploitation sets -- in the exploration set the users receive non-personalized messages and a co-purchase network is updated with user feedback for purchases of the day, while in the exploitation set the updated network is used for recommending personalized messages for the remaining users.A thorough evaluation of our algorithms using real data obtained from a large daily deals website in Brazil in contrast to state-of-the-art recommendation algorithms show gains in precision ranging from 18% to 34%.