Incorporating User Expectations and Behavior into the Measurement of Search Effectiveness

Alistair Moffat
Department of Computing and Information Systems, The University of Melbourne, Victoria 3010, Australia.

Peter Bailey
Microsoft Research, Canberra, Australia.

Falk Scholer
School of Computer Science and Information Technology, RMIT University, Victoria 3001, Australia.

Paul Thomas
Microsoft Research, Canberra Australia.

Status

ACM Trans. Information Systems, 35(3)24:1-24:38, June 2017.

Abstract

Information retrieval systems aim to help users satisfy information needs. We argue that the goal of the person using the system, and the pattern of behavior that they exhibit as they proceed to attain that goal, should be incorporated into the methods and techniques used to evaluate the effectiveness of IR systems, so that the resulting effectiveness scores have a useful interpretation that corresponds to the users' search experience. In particular, we investigate the role of search task complexity, and show that it has a direct bearing on the number of relevant answer documents sought by users in response to an information need, suggesting that useful effectiveness metrics must be goal sensitive. We further suggest that user behavior while scanning results listings is affected by the rate at which their goal is being realized, and hence that appropriate effectiveness metrics must be adaptive to the presence (or not) of relevant documents in the ranking. In response to these two observations, we present a new effectiveness metric, INST, that has both of the desired properties: INST employs a parameter T, a direct measure of the user's search goal that adjusts the top-weightedness of the evaluation score; moreover, as progress towards the target T is made, the modeled user behavior is adapted, to reflect the remaining expectations. INST is experimentally compared to previous effectiveness metrics, including Average Precision (AP), Normalized Discounted Cumulative Gain (NDCG), and Rank-Biased Precision (RBP), demonstrating our claims as to INST's usefulness. Like RBP, INST is a weighted-precision metric, meaning that each score can be accompanied by a residual that quantifies the extent of the score uncertainty caused by unjudged documents. As part of our experimentation, we use crowd-sourced data and score residuals to demonstrate that a wide range of queries arise for even quite specific information needs, and that these variant queries introduce significant levels of residual uncertainty into typical experimental evaluations. These causes of variability have wide-reaching implications for experiment design, and for the construction of test collections.

Full text

http://dx.doi.org/10.1145/3052768

Data Resource

The UQV100 collection that is mentioned in this paper is available from http://dx.doi.org/10.4225/49/5726E597B8376.

Software

An implementation of INST has been prepared by Bevan Koopman, https://github.com/ielab/inst_eval.