Navigating Complex Search Tasks with AI Copilots

As many of us in the information retrieval (IR) research community know and appreciate, search is far from being a solved problem. Millions of people struggle with tasks on search engines every day. Often, their struggles relate to the intrinsic complexity of their task and the failure of search systems to fully understand the task and serve relevant results. The task motivates the search, creating the gap/problematic situation that searchers attempt to bridge/resolve and drives search behavior as they work through different task facets. Complex search tasks require more than support for rudimentary fact finding or re-finding. Research on methods to support complex tasks includes work on generating query and website suggestions, personalizing and contextualizing search, and developing new search experiences, including those that span time and space. The recent emergence of generative artificial intelligence (AI) and the arrival of assistive agents, or copilots, based on this technology, has the potential to offer further assistance to searchers, especially those engaged in complex tasks. There are profound implications from these advances for the design of intelligent systems and for the future of search itself. This article, based on a keynote by the author at the 2023 ACM SIGIR Conference, explores these issues and charts a course toward new horizons in information access guided by AI copilots.Comment: 10 pages, 6 figure

A study of factors affecting the utility of implicit relevance feedback

Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system. IRF is a new method of gathering information on user interest and, if IRF is to be used in operational IR systems, it is important to establish when it performs well and when it performs poorly. In this paper we investigate how the use and effectiveness of IRF is affected by three factors: search task complexity, the search experience of the user and the stage in the search. Our findings suggest that all three of these factors contribute to the utility of IRF

Evaluating advanced search interfaces using established information-seeking model

When users have poorly defined or complex goals search interfaces offering only keyword searching facilities provide inadequate support to help them reach their information-seeking objectives. The emergence of interfaces with more advanced capabilities such as faceted browsing and result clustering can go some way to some way toward addressing such problems. The evaluation of these interfaces, however, is challenging since they generally offer diverse and versatile search environments that introduce overwhelming amounts of independent variables to user studies; choosing the interface object as the only independent variable in a study would reveal very little about why one design out-performs another. Nonetheless if we could effectively compare these interfaces we would have a way to determine which was best for a given scenario and begin to learn why. In this article we present a formative framework for the evaluation of advanced search interfaces through the quantification of the strengths and weaknesses of the interfaces in supporting user tactics and varying user conditions. This framework combines established models of users, user needs, and user behaviours to achieve this. The framework is applied to evaluate three search interfaces and demonstrates the potential value of this approach to interactive IR evaluation

Experience in Social Affective Applications: Methodologies and Case Study

New forms of social affective applications are emerging, bringing with them challenges in design and evaluation. We report on one such application, conveying well-being for both personal and group benefit, and consider why existing methodologies may not be suitable, before explaining and analyzing our proposed approach. We discuss our experience of using and writing about the methodology, in order to invite discussion about its suitability in particular, as well as the more general need for methodologies to examine experience and affect in social, connected situations. As these fields continue to interact, we hope that these discussions serve to aid in studying and learning from these types of application

Implicit feedback for interactive information retrieval

Searchers can find the construction of query statements for submission to Information Retrieval (IR) systems a problematic activity. These problems are confounded by uncertainty about the information they are searching for, or an unfamiliarity with the retrieval system being used or collection being searched. On the World Wide Web these problems are potentially more acute as searchers receive little or no training in how to search effectively. Relevance feedback (RF) techniques allow searchers to directly communicate what information is relevant and help them construct improved query statements. However, the techniques require explicit relevance assessments that intrude on searchers’ primary lines of activity and as such, searchers may be unwilling to provide this feedback. Implicit feedback systems are unobtrusive and make inferences of what is relevant based on searcher interaction. They gather information to better represent searcher needs whilst minimising the burden of explicitly reformulating queries or directly providing relevance information. In this thesis I investigate implicit feedback techniques for interactive information retrieval. The techniques proposed aim to increase the quality and quantity of searcher interaction and use this interaction to infer searcher interests. I develop search interfaces that use representations of the top-ranked retrieved documents such as sentences and summaries to encourage a deeper examination of search results and drive the information seeking process. Implicit feedback frameworks based on heuristic and probabilistic approaches are described. These frameworks use interaction to identify needs and estimate changes in these needs during a search. The evidence gathered is used to modify search queries and make new search decisions such as re-searching the document collection or restructuring already retrieved information. The term selection models from the frameworks and elsewhere are evaluated using a simulation-based evaluation methodology that allows different search scenarios to be modelled. Findings show that the probabilistic term selection model generated the most effective search queries and learned what was relevant in the shortest time. Different versions of an interface that implements the probabilistic framework are evaluated to test it with human subjects and investigate how much control they want over its decisions. The experiment involved 48 subjects with different skill levels and search experience. The results show that searchers are happy to delegate responsibility to RF systems for relevance assessment (through implicit feedback), but not more severe search decisions such as formulating queries or selecting retrieval strategies. Systems that help searchers make these decisions are preferred to those that act directly on their behalf or await searcher action

Overview of the CLEF-2005 cross-language speech retrieval track

The task for the CLEF-2005 cross-language speech retrieval track was to identify topically coherent segments of English interviews in a known-boundary condition. Seven teams participated, performing both monolingual and cross-language searches of ASR transcripts, automatically generated metadata, and manually generated metadata. Results indicate that monolingual search technology is sufficiently accurate to be useful for some purposes (the best mean average precision was 0.18) and cross-language searching yielded results typical of those seen in other applications (with the best systems approximating monolingual mean average precision)

Designing for Schadenfreude (or, how to express well-being and see if you're boring people)

This position paper presents two studies of content not normally expressed in status updates—well-being and status feedback—and considers how they may be processed, valued and used for potential quality-of-life benefits in terms of personal and social reflection and awareness. Do I Tweet Good? (poor grammar intentional) is a site investigating more nuanced forms of status feedback than current microblogging sites allow, towards understanding self-identity, reflection, and online perception. Healthii is a tool for sharing physical and emotional well-being via status updates, investigating concepts of self-reflection and social awareness. Together, these projects consider furthering the value of microblogging on two fronts: 1) refining the online personal/social networking experience, and 2) using the status update for enhancing the personal/social experience in the offline world, and considering how to leverage that online/offline split. We offer results from two different methods of study and target groups—one co-workers in an academic setting, the other followers on Twitter—to consider how microblogging can become more than just a communication medium if it facilitates these types of reflective practice

Contextualised Browsing in a Digital Library's Living Lab

Contextualisation has proven to be effective in tailoring \linebreak search results towards the users' information need. While this is true for a basic query search, the usage of contextual session information during exploratory search especially on the level of browsing has so far been underexposed in research. In this paper, we present two approaches that contextualise browsing on the level of structured metadata in a Digital Library (DL), (1) one variant bases on document similarity and (2) one variant utilises implicit session information, such as queries and different document metadata encountered during the session of a users. We evaluate our approaches in a living lab environment using a DL in the social sciences and compare our contextualisation approaches against a non-contextualised approach. For a period of more than three months we analysed 47,444 unique retrieval sessions that contain search activities on the level of browsing. Our results show that a contextualisation of browsing significantly outperforms our baseline in terms of the position of the first clicked item in the result set. The mean rank of the first clicked document (measured as mean first relevant - MFR) was 4.52 using a non-contextualised ranking compared to 3.04 when re-ranking the result lists based on similarity to the previously viewed document. Furthermore, we observed that both contextual approaches show a noticeably higher click-through rate. A contextualisation based on document similarity leads to almost twice as many document views compared to the non-contextualised ranking.Comment: 10 pages, 2 figures, paper accepted at JCDL 201

0 Search and Breast Cancer: On Episodic Shifts of Attention over Life Histories of an Illness

We seek to understand the evolving needs of people who are faced with a life-changing medical diagnosis based on analyses of queries extracted from an anonymized search query log. Focusing on breast cancer, we manually tag a set of Web searchers as showing patterns of search behavior consistent with someone grappling with the screening, diagnosis, and treatment of breast cancer. We build and apply probabilistic classifiers to detect these searchers from multiple sessions and to identify the timing of diagnosis using temporal and statistical features. We explore the changes in information-seeking over time before and after an inferred diagnosis of breast cancer by aligning multiple searchers by the estimated time of diagnosis. We employ the classifier to automatically identify 1700 candidate searchers with an estimated 90% precision, and we predict the day of diagnosis within 15 days with an 88% accuracy. We show that the geographic and demographic attributes of searchers identified with high probability are strongly correlated with ground truth of reported incidence rates. We then analyze the content of queries over time for inferred cancer patients, using a detailed ontology of cancer-related search terms. The analysis reveals the rich temporal structure of the evolving queries of people likely diagnosed with breast cancer. Finally, we focus on subtypes of illness based on inferred stages of cancer and show clinically relevant dynamics of information seeking based on the dominant stage expressed by searchers