Search

This article presents a current state of search, where it is lacking, and how artificial intelligence (AI) assistant agents can improve it. The article details the role of tasks in search, defines and classifies complex search tasks, describes AI agents that can improve the process and explores adding them to search engines. In addition, challenges and opportunities are presented that can serve to redefine information access and use.

Search and recommender systems should prioritize support for user tasks over support for individual queries or actions. To that end, the Information Retrieval (IR) community has spent decades trying to understand users' tasks and their contexts, and how to best assist users in making progress toward completing them. The recent advancements in generative artificial intelligence (AI) have drastically shifted the landscape of task-focused IR. Users can now express not only their queries and questions, but actual information needs, tasks, and goals in natural language to an AI system and receive not just results, but also answers in natural language that are generated specifically for them. This new paradigm raises many interesting questions, opportunities, and challenges. We brought together a group of highly motivated students and scholars in a two-day workshop on the Microsoft campus in Redmond to discuss these issues, learn from each other, and envision a new future for task-focused IR and information access more broadly. Date: 28--29 September 2023. Website: https://ir-ai.github.io. [ABSTRACT FROM AUTHOR]

Seeking to address a wider range of user requests toward task completion. [ABSTRACT FROM AUTHOR]

The authors propose the concept of slow search wherein search engines use more time to provide a better search experience than is possible with conventional time constraints. Topics discussed include people's perception that search engine results that are provided quickly are higher quality and more engaging, advances in the understanding of how people search for information, and ways in which slow search can enable search engines to relax restrictions and provide better search results.

This article focuses on research and development opportunities to improve current Internet search interfaces so users can succeed more often in situations when they lack the knowledge or contextual awareness to formulate queries or navigate complex information spaces, the search task requires browsing and exploration, or system indexing of available information is inadequate. Most people rely on tentative queries, exploring and retrieving information, and selectively seeking and passively obtaining cues about what step to take next. Researchers are working on techniques to support these kinds of queries in what is known as "exploratory search."

This report describes the 2010 workshop on Human-Computer Interaction and Information Retrieval. Now in its fourth year, the event was held in August 2010 in conjunction with the Information Interaction in Context Symposium. The workshop brought together researchers from academia, industry, and government and a range of disciplines to present and discuss their research. We had a record 70 attendees, making this the largest of our workshops to date. New for this year, we ran a challenge, and six research groups participated. [ABSTRACT FROM AUTHOR]

This report describes HCIR 2009, the third international workshop on Human-Computer Interaction and Information Retrieval (HCIR), held in October 2009 at The Catholic University of America in Washington, DC. The workshop attracted over 50 participants from across the world and was the largest HCIR workshop to date. The event brought together representatives from academia, industry, and government to discuss research, present work in process, and advance ideas in the area of HCIR. The workshop consisted of a keynote presentation, panel presentations, a poster session, and two guided discussion sessions. [ABSTRACT FROM AUTHOR]

A conference paper about Web information seeking and interaction (WISI), pertaining to the interaction of users with Web-based content and applications during information-seeking activities, is presented. The international program committee of the Special Interest Group on Information Retrieval (SIGIR), Association for Computing Machinery (ACM) invited Peter Anick of Yahoo!, Diane Kelly of the University of North Carolina and Maria Stone of Google to speak on the issue.

Exploratory search systems (ESS) are designed to help users move beyond simply finding information toward using that information to support learning, analysis, and decision-making. The evaluation of the interactive systems designed specifically to help exploratory searchers is a challenging area, worthy of further discussion in the research community. In this article we report on a workshop conducted in conjunction with the ACM SIGIR Conference in Seattle, USA, in August 2006. The workshop involved researchers, academics, and practitioners discussing the formative and summative evaluation of ESS. [ABSTRACT FROM AUTHOR]

The development and testing of systems to support users engaged in exploratory search activities (i.e., searches where the target may be undefined) is an challenge for the online search community. In this article we report on a workshop on exploratory search issues organized in conjunction with the University of Maryland Human-Computer Interaction Laboratory's Annual Symposium and Open House in June 2005. This workshop brought together researchers from the fields of Information Seeking (IS), Information Retrieval (IR), Human-Computer Interaction (HCI) and Information Visualization (IV) for a cross-disciplinary exploration of these and other issues. Although originally intended to focus on interfaces to support exploratory search the workshop blossomed into a rich discussion of not only interface issues, but also evaluation, the cognitive processes that underlie information exploration and research methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The task in expert finding is to identify members of an organisation with relevant expertise on a given topic. Typically, an expert search engine uses evidence from the authors of on-topic documents found in the organisation's intranet by search engines. The search result click-through behaviour of many intranet search engine users provides an additional source of evidence to identify topically-relevant documents, and via document authorship, experts. In this poster, we assess the usefulness of click-through log data for expert finding. We find that ranking authors based solely on the clicks their documents receive is reasonably effective at correctly identifying relevant experts. Moreover, we show that this evidence can successfully be integrated with an existing expert search engine to increase its retrieval effectiveness. [ABSTRACT FROM AUTHOR]

In this poster, we describe the study of an interface technique that provides a list of suggested additional query terms as a searcher types a search query, in effect offering interactive query expansion (IQE) options while the query is formulated. Analysis of the results shows that offering IQE during query formulation leads to better quality initial queries, and an increased uptake of query expansion. These findings have implications for how IQE should be offered in retrieval interfaces. [ABSTRACT FROM AUTHOR]

Historians and scholars can better understand historic events by studying the geographic and chronological activity of individuals who witnessed them. A lack of adequate tools to help users study these activities can hinder the process of learning and discovery, In this paper we present an interface to address this problem that contains three components: a map, a timeline, and a text representation of a survivor's movements. These components simultaneously provide query input (where users can specify their needs) and dynamic results display (where users can immediately see the effect of their decisions). The results of a pilot study show that users reacted positively to the interface. [ABSTRACT FROM AUTHOR]

In this poster we describe an investigation of topic similarity measures. We elicit assessments on the similarity of 10 pairs of topic from 76 subjects and use these as a benchmark to assess how well each measure performs. The measures have the potential to form the basis of a predictive technique, for adaptive search systems. The results of our evaluation show that measures based on the level of correlation between topics concords most with general subject perceptions of search topic similarity. [ABSTRACT FROM AUTHOR]

The article provides information on the 2011 Workshop on Human-Computer Interaction and Information Retrieval which was held in Mountain View, California in October 2011. This event serves as a venue for in-depth discussion of models, tools and evaluation methods involving human factors and search. It also features the HCIR Challenge where the participants built systems used in addressing the information availability problem with the use of CiteSeer data.

This article presents an abstract of the paper Implicit Feedback for Interactive Information Retrieval, by Ryen W. White. This thesis investigates 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. Search interfaces are developed 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 the needs and estimate changes in these needs during a search. 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 implicit feedback is best implemented at the interface. The results show that searchers are happy to delegate responsibility to relevance feedback systems for relevance assessment, but not more severe search decisions such as formulating queries or selecting retrieval strategies.

We demonstrate an adaptive search system that works proactively to help searchers find relevant information. The system observes searcher interaction, uses what it sees to model information needs and chooses additional query terms. The system watches for changes in the topic of the search and selects retrieval strategies that reflect the extent to which the topic is seen to change. [ABSTRACT FROM AUTHOR]