Current Research in Supporting Complex Search Tasks

ABSTRACT ere is broad consensus in the eld of IR that search is complex in many use cases and applications, both on the Web and in domain speci c collections, and both professionally and in our daily life. Yet our understanding of complex search tasks, in comparison to simple look up tasks, is fragmented at best. e workshop addresses many open research questions: What are the obvious use cases and applications of complex search? What are essential features of work tasks and search tasks to take into account? And how do these evolve over time? With a multitude of information, varying from introductory to specialized, and from authoritative to speculative or opinionated, when to show what sources of information? How does the information seeking process evolve and what are relevant di erences between di erent stages? With complex task and search process management, blending searching, browsing, and recommendations, and supporting exploratory search to sensemaking and analytics, UI and UX design pose an overconstrained challenge. How do we evaluate and compare approaches? Which measures should be taken into account? Supporting complex search tasks requires new collaborations across the elds of CHI and IR, and the proposed workshop will bring together a diverse group of researchers to work together on one of the greatest challenges of our eld

Veebi otsingumootorid ja vajadus keeruka informatsiooni järele

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Veebi otsingumootorid on muutunud põhiliseks teabe hankimise vahenditeks internetist. Koos otsingumootorite kasvava populaarsusega on nende kasutusala kasvanud lihtsailt päringuilt vajaduseni küllaltki keeruka informatsiooni otsingu järele. Samas on ka akadeemiline huvi otsingu vastu hakanud liikuma lihtpäringute analüüsilt märksa keerukamate tegevuste suunas, mis hõlmavad ka pikemaid ajaraame. Praegused otsinguvahendid ei toeta selliseid tegevusi niivõrd hästi nagu lihtpäringute juhtu. Eriti kehtib see toe osas koondada mitme päringu tulemusi kokku sünteesides erinevate lihtotsingute tulemusi ühte uude dokumenti. Selline lähenemine on alles algfaasis ja ning motiveerib uurijaid arendama vastavaid vahendeid toetamaks taolisi informatsiooniotsingu ülesandeid. Käesolevas dissertatsioonis esitatakse rida uurimistulemusi eesmärgiga muuta keeruliste otsingute tuge paremaks kasutades tänapäevaseid otsingumootoreid. Alameesmärkideks olid: (a) arendada välja keeruliste otsingute mudel, (b) mõõdikute loomine kompleksotsingute mudelile, (c) eristada kompleksotsingu ülesandeid lihtotsingutest ning teha kindlaks, kas neid on võimalik mõõta leides ühtlasi lihtsaid mõõdikuid kirjeldamaks nende keerukust, (d) analüüsida, kui erinevalt kasutajad käituvad sooritades keerukaid otsinguülesandeid kasutades veebi otsingumootoreid, (e) uurida korrelatsiooni inimeste tava-veebikasutustavade ja nende otsingutulemuslikkuse vahel, (f) kuidas inimestel läheb eelhinnates otsinguülesande raskusastet ja vajaminevat jõupingutust ning (g) milline on soo ja vanuse mõju otsingu tulemuslikkusele. Keeruka veebiotsingu ülesanded jaotatakse edukalt kolmeastmeliseks protsessiks. Esitatakse sellise protsessi mudel; seda protsessi on ühtlasi võimalik ka mõõta. Edasi näidatakse kompleksotsingu loomupäraseid omadusi, mis teevad selle eristatavaks lihtsamatest juhtudest ning näidatakse ära katsemeetod sooritamaks kompleksotsingu kasutaja-uuringuid. Demonstreeritakse põhilisi samme raamistiku “Search-Logger” (eelmainitud metodoloogia tehnilise teostuse) rakendamisel kasutaja-uuringutes. Esitatakse sellisel viisil teostatud uuringute tulemused. Lõpuks esitatakse ATMS meetodi realisatsioon ja rakendamine parandamaks kompleksotsingu vajaduste tuge kaasaegsetes otsingumootorites.Search engines have become the means for searching information on the Internet. Along with the increasing popularity of these search tools, the areas of their application have grown from simple look-up to rather complex information needs. Also the academic interest in search has started to shift from analyzing simple query and response patterns to examining more sophisticated activities covering longer time spans. Current search tools do not support those activities as well as they do in the case of simple look-up tasks. Especially the support for aggregating search results from multiple search-queries, taking into account discoveries made and synthesizing them into a newly compiled document is only at the beginning and motivates researchers to develop new tools for supporting those information seeking tasks. In this dissertation I present the results of empirical research with the focus on evaluating search engines and developing a theoretical model of the complex search process that can be used to better support this special kind of search with existing search tools. It is not the goal of the thesis to implement a new search technology. Therefore performance benchmarks against established systems such as question answering systems are not part of this thesis. I present a model that decomposes complex Web search tasks into a measurable, three-step process. I show the innate characteristics of complex search tasks that make them distinguishable from their less complex counterparts and showcase an experimentation method to carry out complex search related user studies. I demonstrate the main steps taken during the development and implementation of the Search-Logger study framework (the technical manifestation of the aforementioned method) to carry our search user studies. I present the results of user studies carried out with this approach. Finally I present development and application of the ATMS (awareness-task-monitor-share) model to improve the support for complex search needs in current Web search engines

Supporting Exploratory Search Tasks Through Alternative Representations of Information

Information seeking is a fundamental component of many of the complex tasks presented to us, and is often conducted through interactions with automated search systems such as Web search engines. Indeed, the ubiquity of Web search engines makes information so readily available that people now often turn to the Web for all manners of information seeking needs. Furthermore, as the range of online information seeking tasks grows, more complex and open-ended search activities have been identified. One type of complex search activities that is of increasing interest to researchers is exploratory search, where the goal involves "learning" or "investigating", rather than simply "looking-up". Given the massive increase in information availability and the use of online search for tasks beyond simply looking-up, researchers have noted that it becomes increasingly challenging for users to effectively leverage the available online information for complex and open-ended search activities. One of the main limitations of the current document retrieval paradigm offered by modern search engines is that it provides a ranked list of documents as a response to the searcher’s query with no further support for locating and synthesizing relevant information. Therefore, the searcher is left to find and make sense of useful information in a massive information space that lacks any overview or conceptual organization. This thesis explores the impact of alternative representations of search results on user behaviors and outcomes during exploratory search tasks. Our inquiry is inspired by the premise that exploratory search tasks require sensemaking, and that sensemaking involves constructing and interacting with representations of knowledge. As such, in order to provide the searchers with more support in performing exploratory activities, there is a need to move beyond the current document retrieval paradigm by extending the support for locating and externalizing semantic information from textual documents and by providing richer representations of the extracted information coupled with mechanisms for accessing and interacting with the information in ways that support exploration and sensemaking. This dissertation presents a series of discrete research endeavour to explore different aspects of providing information and presenting this information in ways that both extraction and assimilation of relevant information is supported. We first address the problem of extracting information – that is more granular than documents – as a response to a user's query by developing a novel information extraction system to represent documents as a series of entity-relationship tuples. Next, through a series of designing and evaluating alternative representations of search results, we examine how this extracted information can be represented such that it extends the document-based search framework's support for exploratory search tasks. Finally, we assess the ecological validity of this research by exploring error-prone representations of search results and how they impact a searcher's ability to leverage our representations to perform exploratory search tasks. Overall, this research contributes towards designing future search systems by providing insights into the efficacy of alternative representations of search results for supporting exploratory search activities, culminating in a novel hybrid representation called Hierarchical Knowledge Graphs (HKG). To this end we propose and develop a framework that enables a reliable investigation of the impact of different representations and how they are perceived and utilized by information seekers

THE EFFECTS OF ALTERNATE-LINE SHADING ON VISUAL SEARCH IN GRID-BASED GRAPHIC DESIGNS

Objective: The goal of this research was to determine whether alternate-line shading (zebra-striping) of grid-based displays affects the strategy (i.e., “visual flow”) and efficiency of serial search. Background: Grids, matrices, and tables are commonly used to organize information. A number of design techniques and psychological principles are relevant to how viewers’ eyes can be guided through such visual works. One common technique for grids, “zebra-striping,” is intended to guide eyes through the design, or “create visual flow” by alternating shaded and unshaded rows or columns. Method: 13 participants completed a visual serial search task. The target was embedded in a grid that had 1) no shading, 2) shading of alternating rows, or 3) shading of alternating columns. Response times and error rates were analyzed to determine search strategy and efficiency. Results: Our analysis found evidence supporting a weak effect of shading on search strategy. The direction of shading had an impact on which parts of the grid were responded to most rapidly. However, a left-to-right reading bias and middle-to-outside edge effect were also found. Overall performance was reliably better when the grid had no shading. Exploratory analyses suggest individual differences may be a factor. Conclusion: Shading seems to create visual flow that is relatively weak compared to search strategies related to the edge effect or left-to-right reading biases. In general, however, the presence of any type of shading reduced search performance. Application: Designers creating a grid-based display should not automatically assume that shading will change viewers search strategies. Furthermore, although strategic shading may be useful for tasks other than that studied here, our current data indicate that shading can actually be detrimental to visual search for complex (i.e., conjunctive) targets

Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach

A significant amount of search queries originate from some real world information need or tasks. In order to improve the search experience of the end users, it is important to have accurate representations of tasks. As a result, significant amount of research has been devoted to extracting proper representations of tasks in order to enable search systems to help users complete their tasks, as well as providing the end user with better query suggestions, for better recommendations, for satisfaction prediction, and for improved personalization in terms of tasks. Most existing task extraction methodologies focus on representing tasks as flat structures. However, tasks often tend to have multiple subtasks associated with them and a more naturalistic representation of tasks would be in terms of a hierarchy, where each task can be composed of multiple (sub)tasks. To this end, we propose an efficient Bayesian nonparametric model for extracting hierarchies of such tasks \& subtasks. We evaluate our method based on real world query log data both through quantitative and crowdsourced experiments and highlight the importance of considering task/subtask hierarchies.Comment: 10 pages. Accepted at SIGIR 2017 as a full pape