Adapting ranking functions to user preference

Learning to rank has become a popular method for web search ranking. Traditionally, expert-judged examples are the major training resource for machine learned web ranking, which is expensive to get for training a satisfactory ranking function. The demands for generating specific web search ranking functions tailored for different domains, such as ranking functions for different regions, have aggravated this problem. Recently, a few methods have been proposed to extract training examples from user click through log. Due to the low cost of getting user preference data, it is attractive to combine these examples in training ranking functions. However, because of the different natures of the two types of data, they may have different influences on ranking function. Therefore, it is challenging to develop methods for effectively combining them in training ranking functions. In this paper, we address the problem of adapting an existing ranking function to user preference data, and develop a framework for conveniently tuning the contribution of the user preference data in the tuned ranking function. Experimental results show that with our framework it is convenient to generate a batch of adapted ranking functions and to select functions with different tradeoffs between the base function and the user preference data.