Optimal Strategies for Reviewing Search Results - Huang, Jeff; Kazeykina, Anna (Proceedings of the AAAI Conference on Artificial Intelligence, 2010)
Web search engines respond to a query by returning more results than can be reasonably reviewed. These results typically include the title, link, and snippet of content from the target link. Each result has the potential to be useful or useless and thus reviewing it has a cost and potential benefit. This paper studies the behavior of a rational agent in this setting, whose objective is to maximize the probability of finding a satisfying result while minimizing cost. We propose two similar agents with different capabilities: one that only compares result snippets relatively and one that predicts from the result snippet whether the result will be satisfying. We prove that the optimal strategy for both agents is a stopping rule: the agent reviews a fixed number of results until the marginal cost is greater than the marginal expected benefit, maximizing the overall expected utility. Finally, we discuss the relationship between rational agents and search users and how our findings help us understand reviewing behaviors.