Traditionally, the efficiency and effectiveness of search systems have both been of great interest to the information retrieval community. However, an in-depth analysis on the interplay between the response latency of web search systems and users' search experience has been missing so far. In order to fill this gap, we conduct two separate studies aiming to reveal how response latency affects the user behavior in web search. First, we conduct a controlled user study trying to understand how users perceive the response latency of a search system and how sensitive they are to increasing delays in response. This study reveals that, when artificial delays are introduced into the response, the users of a fast search system are more likely to notice these delays than the users of a slow search system. The introduced delays become noticeable by the users once they exceed a certain threshold value. Second, we perform an analysis using a large-scale query log obtained from Yahoo web search to observe the potential impact of increasing response latency on the click behavior of users. This analysis demonstrates that latency has an impact on the click behavior of users to some extent. In particular, given two content-wise identical search result pages, we show that the users are more likely to perform clicks on the result page that is served with lower latency.

https://s.yimg.com/ge/labs/v2/uploads/fp482-arapakis.pdf

Impact of response latency on user behavior in web search

Understanding how people interact when searching is central to the study of Interactive Information Retrieval (IIR). Most of the prior work has either been conceptual, observational or empirical. While this has led to numerous insights and findings regarding the interaction between users and systems, the theory has lagged behind. In this paper, we extend the recently proposed search economic theory to make the model more realistic. We then derive eight interaction based hypotheses regarding search behaviour. To validate the model, we explore whether the search behaviour of thirty-six participants from a lab based study is consistent with the theory. Our analysis shows that observed search behaviours are in line with predicted search behaviours and that it is possible to provide credible explanations for such behaviours. This work describes a concise and compact representation of search behaviour providing a strong theoretical basis for future IIR research.

/pdf/modelling-interaction-with-economic-models-of-search-4o2zv5hs5a.pdf

Modelling interaction with economic models of search

We introduce a general information access evaluation framework that can potentially handle summaries, ranked document lists and even multi query sessions seamlessly. Our framework first builds a trailtext which represents a concatenation of all the texts read by the user during a search session, and then computes an evaluation metric called U-measure over the trailtext. Instead of discounting the value of a retrieved piece of information based on ranks, U-measure discounts it based on its position within the trailtext. U-measure takes the document length into account just like Time-Biased Gain (TBG), and has the diminishing return property. It is therefore more realistic than rank-based metrics. Furthermore, it is arguably more flexible than TBG, as it is free from the linear traversal assumption (i.e., that the user scans the ranked list from top to bottom), and can handle information access tasks other than ad hoc retrieval. This paper demonstrates the validity and versatility of the U-measure framework. Our main conclusions are: (a) For ad hoc retrieval, U-measure is at least as reliable as TBG in terms of rank correlations with traditional metrics and discriminative power; (b) For diversified search, our diversity versions of U-measure are highly correlated with state-of-the-art diversity metrics; (c) For multi-query sessions, U-measure is highly correlated with Session nDCG; and (d) Unlike rank-based metrics such as DCG, U-measure can quantify the differences between linear and nonlinear traversals in sessions. We argue that our new framework is useful for understanding the user's search behaviour and for comparison across different information access styles (e.g. examining a direct answer vs. examining a ranked list of web pages).

Summaries, ranked retrieval and sessions: a unified framework for information access evaluation

affects how users interact with a search system. Microeconomic theory is used to generate the cost-interaction hypothesis that states as the cost of querying increases, users will pose fewer queries and examine more documents per query. A between-subjects laboratory study with 36 undergraduate subjects was conducted, where subjects were randomly assigned to use one of three search interfaces that varied according to the amount of physical cost required to query: Structured (high cost), Standard (medium cost) and Query Suggestion (low cost). Results show that subjects who used the Structured interface submitted significantly fewer queries, spent more time on search results pages, examined significantly more documents per query, and went to greater depths in the search results list. Results also showed that these subjects spent longer generating their initial queries, saved more relevant documents and rated their queries as more successful. These findings have implications for the usefulness of microeconomic theory as a way to model and explain search interaction, as well as for the design of query facilities.

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How query cost affects search behavior

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.

Incorporating User Expectations and Behavior into the Measurement of Search Effectiveness

Real life information retrieval takes place in sessions, where users search by iterating between various cognitive, perceptual and motor subtasks through an interactive interface. The sessions may follow diverse strategies, which, together with the interface characteristics, affect user effort (cost), experience and session effectiveness. In this paper we propose a pragmatic evaluation approach based on scenarios with explicit subtask costs. We study the limits of effectiveness of diverse interactive searching strategies in two searching environments (the scenarios) under overall cost constraints. This is based on a comprehensive simulation of 20 million sessions in each scenario. We analyze the effectiveness of the session strategies over time, and the properties of the most and the least effective sessions in each case. Furthermore, we will also contrast the proposed evaluation approach with the traditional one, rank based evaluation, and show how the latter may hide essential factors that affect users' performance and satisfaction - and gives even counter-intuitive results.

Time drives interaction: simulating sessions in diverse searching environments

Evaluation is central in research and development of information retrieval (IR). In addition to designing and implementing new retrieval mechanisms, one must also show through rigorous evaluation that they are effective. A major focus in IR is IR mechanisms’ capability of ranking relevant documents optimally for the users, given a query. Searching for information in practice involves searchers, however, and is highly interactive. When human searchers have been incorporated in evaluation studies, the results have often suggested that better ranking does not necessarily lead to better search task, or work task, performance. Therefore, it is not clear which system or interface features should be developed to improve the effectiveness of human task performance. In the present article, we focus on the evaluation of task-based information interaction (TBII). We give special emphasis to learning tasks to discuss TBII in more concrete terms. Information interaction is here understood as behavioral and cognitive activities related to task planning, searching information items, selecting between them, working with them, and synthesizing and reporting. These five generic activities contribute to task performance and outcome and can be supported by information systems. In an attempt toward task-based evaluation, we introduce program theory as the evaluation framework. Such evaluation can investigate whether a program consisting of TBII activities and tools works and how it works and, further, provides a causal description of program (in)effectiveness. Our goal in the present article is to structure TBII on the basis of the five generic activities and consider the evaluation of each activity using the program theory framework. Finally, we combine these activity-based program theories in an overall evaluation framework for TBII. Such an evaluation is complex due to the large number of factors affecting information interaction. Instead of presenting tested program theories, we illustrate how the evaluation of TBII should be accomplished using the program theory framework in the evaluation of systems and behaviors, and their interactions, comprehensively in context.

Task-Based Information Interaction Evaluation: The Viewpoint of Program Theory

In real-life, information retrieval consists of sessions of one or more query iterations. Each iteration has several subtasks like query formulation, result scanning, document link clicking, document reading and judgment, and stopping. Each of the subtasks has behavioral factors associated with them. These factors include search goals and cost constraints, query formulation strategies, scanning and stopping strategies, and relevance assessment behav-ior. Traditional IR evaluation focuses on retrieval and result presentation methods, and interaction within a single-query session. In the present study we aim at assessing the effects of the behavioral factors on retrieval effectiveness. Our research questions include how effective is human behavior employing search strategies compared to various baselines under various search goals and time constraints. We examine both ideal as well as fallible human behavior and wish to identify robust behaviors, if any. Methodologically, we use extensive simulation of human behavior in a test collection. Our findings include that (a) human behavior using multi-query sessions may exceed in effectiveness comparable single-query sessions, (b) the same empirically observed behavioral patterns are reasonably effective under various search goals and constraints, but (c) remain on average clearly below the best possible ones. Moreover, there is no behavioral pattern for sessions that would be even close to winning in most cases; the information need (or topic) in relation to the test collection is a determining factor.

Modeling behavioral factors ininteractive information retrieval

Much of the research in relevance feedback (RF) has been performed under laboratory conditions using test collections and either test persons or simple simulation These studies have given mixed results The design of the present study is unique First, the initial queries are realistically short queries generated by real end-users Second, we perform a user simulation with several RF scenarios Third, we simulate human fallibility in providing RF, ie, incorrectness in feedback Fourth, we employ graded relevance assessments in the evaluation of the retrieval results The research question is: how does RF affect IR performance when initial queries are short and feedback is fallible? Our findings indicate that very fallible feedback is no different from pseudorelevance feedback (PRF) and not effective on short initial queries However, RF with empirically observed fallibility is as effective as correct RF and able to improve the performance of short initial queries

Simulating simple and fallible relevance feedback

This paper describes empirical results of information retrieval in 13 languages of the Cross Language Evaluation Forum (CLEF) collection augmented with results of Turkish using syllables as a means to manage morphological variation in the languages. This kind of approach has been used in speech retrieval [1], but for some reason it has not been much tried out in text-based IR, although it has many clear advantages. Firstly, a quite well working version of it can be implemented with a very simple syllabification algorithm, consisting of only variants of one syllable structure rule, CV, consonant vowel. Secondly, although syllable-based word form variation management resembles n-gramming [2], it has the advantage, that the number of grams with syllables is more restricted which keeps the size of the text index smaller and retrieval faster. Thirdly, syllable-based approach makes possible to use different types of syllabification procedures, which can be either very fine grained, i.e. language specific or very coarse, i.e. more language independent. Fourthly, syllable based methods work for both speech and text retrieval. Our results show, that the two different CV syllabification procedures produced good results with four morphologically complex languages of the CLEF collection. For Turkish they produced also good results. For three of the languages that got good results with the CV syllabification (De, Fi and Tu), we tried also language specific, accurate syllabification procedures. Accurate syllabification was not able to produce as good IR results as CV procedures, but it was not far behind in performance.

Feza Baskaya

Papers

Time drives interaction: simulating sessions in diverse searching environments

Task-Based Information Interaction Evaluation: The Viewpoint of Program Theory

Modeling behavioral factors ininteractive information retrieval

Simulating simple and fallible relevance feedback

Using Syllables As Indexing Terms in Full-Text Information Retrieval