Adapting Rankers Online
At the heart of many effective approaches to the core information retrieval problem - identifying relevant content - lays the following three-fold strategy: obtaining content-based matches, inferring additional ranking criteria and constraints, and combining all of the above so as to arrive at a single ranking of retrieval units.
As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune the parameters for integrating multiple ways of ranking documents. Using online learning to rank approaches, retrieval systems can learn directly from interactions with users, while they are running. Such systems can continuously adapt to user preferences throughout their lifetime, leading to better search performance in settings where expensive manual tuning is infeasible.
Maarten de Rijke about his presentation: «In the talk I will focus on two issues related to online learning to rank. First, I will discuss the issue of balancing exploitation (that is, using what has been learned so far) and exploration (i.e., trying our alternatives so as to learn effectively). Second, present a new method for comparing retrieval functions using implicit feedback. Our method is based on a probabilistic model of such comparisons. Our analytical and experimental results show that our method is more accurate, and more robust to noise than existing methods».
О докладчике: Maarten de Rijke, Full professor Information Processing and Internet (University of Amsterdam), Director Center for Creation, Content and Technology, Director Intelligent Systems Lab Amsterdam.
ВНИМАНИЕ! Докладчик будет читать на английском языке, но свои вопросы на английском или русском языке вы можете задать уже сегодня.
