. Alternatively, novel measures based on graded relevance assessments may be developed. This paper proposes three novel measures that compute the cumulative gain the user obtains by examining the retrieval result up to a given ranked position. The first one accumulates the relevance scores of retrieved documents

of documents of different relevance grades and (2) to weight the importance of gain values at different ranks, respectively. In this work we discuss how to empirically derive a gain and discount function that optimizes the efficiency or stability of nDCG. First, we describe a variance decomposition analysis

This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning

environments. The proposed methods are (1) a novel application of P-R curves and average precision computations based on separate recall bases for documents of different degrees of relevance, and (2) two novel measures computing the cumulative gain the user obtains by examining the retrieval result up to a

Web search engines are traditionally evaluated in terms of the relevance of web pages to individual queries. However, relevance of web pages does not tell the complete picture, since an individual query may represent only a piece of the user’s information need and users may have different

We formulate and study search algorithms that consider a user’s prior interactions with a wide variety of content to personalize that user’s current Web search. Rather than relying on the unrealistic assumption that people will precisely specify their intent when searching, we pursue techniques

the performance of ranking functions using DCG very much depends on the particular gain values and discount factors used. We then propose a novel methodology that can learn the gain values and discount factors from user preferences over rankings, modeled as a special case of learning linear utility functions. We

the risk of dissatisfaction of the average user. We propose an algorithm that well approximates this objective in general, and is provably optimal for a natural special case. Furthermore, we generalize several classical IR metrics, including NDCG, MRR, and MAP, to explicitly account for the value

difference is high, the effect on user experience will be low. Time-biased gain is an evaluation metric that models user interaction with ranked lists that are displayed using document surrogates. In this paper, we extend the stochastic simulation of time-biased gain to model the variation between users. We

The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people’s location histories. In this paper, based on multiple users ’ GPS trajectories, we aim to mine interesting locations