Section: Application Domains
We work on the news/ad recommendation. These online learning algorithms reached a critical importance over the last few years due to these major applications. After designing a new algorithm, it is critical to be able to evaluate it without having to plug it into the real application in order to protect user experiences or/and the company's revenue. To do this, people used to build simulators of user behaviors and try to achieve good performances against it. However designing such a simulator is probably much more difficult than designing the algorithm itself! An other common way to evaluate is to not consider the exploration/exploitation dilemma (also known as “Cold Start” for recommender systems). Lately data-driven methods have been developed. We are working on building automatic replay methodology with some theoretical guarantees. This work also exhibits strong link with the choice of the number of contexts to use with recommender systems wrt your audience.
An other point is that web sites must forecast Web page views in order to plan computer resource allocation and estimate upcoming revenue and advertising growth. In this work, we focus on extracting trends and seasonal patterns from page view series. We investigate Holt-Winters/ARIMA like procedures and some regularized models for making short-term prediction (3-6 weeks) wrt to logged data of several big media websites. We work on some news event related webpages and we feel that kind of time series deserves a particular attention. Self-similarity is found to exist at multiple time scales of network traffic, and can be exploited for prediction. In particular, it is found that Web page views exhibit strong impulsive changes occasionally. The impulses cause large prediction errors long after their occurrences and can sometimes be predicted (e.g., elections, sport events, editorial changes,holidays) in order to improve accuracies. It also seems that some promising model could arise from using global trends shift in the population.