Is Exploratory Search Different? : A Comparison of Information Search Behavior for Exploratory and Lookup Tasks

Exploratory search is an increasingly important activity yet challenging for users. Although there exists an ample amount of research into understanding exploration, most of the major information retrieval (IR) systems do not provide tailored and adaptive support for such tasks. One reason is the lack of empirical knowledge on how to distinguish exploratory and lookup search behaviors in IR systems. The goal of this paper is to investigate how to separate the two types of tasks in an IR system using easily measurable behaviors. In this paper, we first review characteristics of exploratory search behavior. We then report on a controlled study of six search tasks with three exploratory – comparison, knowledge acquisition, planning – and three lookup tasks – fact-finding, navigational, question answering. The results are encouraging, showing that IR systems can distinguish the two search categories in the course of a search session. The most distinctive indicators that characterize exploratory search behaviors are query length, maximum scroll depth, and task completion time. However, two tasks are borderline and exhibit mixed characteristics. We assess the applicability of this finding by reporting on several classification experiments. Our results have valuable implications for designing tailored and adaptive IR systems.Peer reviewe

Information Search as Adaptive Interaction

We use information retrieval (IR) systems to meet a broad range of information needs, from simple ones involving day-to-day decisions to complex and imprecise information needs that cannot be easily formulated as a question. In consideration of these diverse goals, search activities are commonly divided into two broad categories: lookup and exploratory. Lookup searches begin with precise search goals and end soon after reaching of the target, while exploratory searches center on learning or investigation activities with imprecise search goals. Although exploration is a prominent life activity, it is naturally challenging for users because they lack domain knowledge; at the same time, information needs are broad, complex, and subject to constant change. It is also rather difficult for IR systems to offer support for exploratory searches, not least because of the complex information needs and dynamic nature of the user. It is hard also to conceptualize exploration distinctly. In consequence, most of the popular IR systems are targeted at lookup searches only. There is a clear need for better IR systems that support a wide range of search activities. The primary objective for this thesis is to enable the design of IR systems that support exploratory and lookup searches equally well. I approached this problem by modeling information search as a rational adaptation of interactions, which aids in clear conceptualization of exploratory and lookup searches. In work building on an existing framework for examination of adaptive interaction, it is assumed that three main factors influence how we interact with search systems: the ecological structure of the environment, our cognitive and perceptual limits, and the goal of optimizing the tradeoff between information gain and time cost. This thesis contributes three models developed in research proceeding from this adaptive interaction framework, to 1) predict evolving information needs in exploratory searches, 2) distinguish between exploratory and lookup tasks, and 3) predict the emergence of adaptive search strategies. It concludes with development of an approach that integrates the proposed models for the design of an IR system that provides adaptive support for both exploratory and lookup searches. The findings confirm the ability to model information search as adaptive interaction. The models developed in the thesis project have been empirically validated through user studies, with an adaptive search system that emphasizes the practical implications of the models for supporting several types of searches. The studies conducted with the adaptive search system further confirm that IR systems could improve information search performance by dynamically adapting to the task type. The thesis contributes an approach that could prove fruitful for future IR systems in efforts to offer more efficient and less challenging search experiences.Tiedonhakujärjestelmiä käytetään monenlaisiin tarkoituksiin, niin vastausten hakemiseen yksinkertaisiin arkipäivän kysymyksiin kuin laajemman ja monipuolisen tiedon etsimisen aiheista, joita ei voida helposti esittää hakulausekkeen muodossa. Kun otetaan huomioon nämä käyttäjien erilaisten tiedon etsintätarpeet, voidaan tiedon etsintä yleisesti jakaa yleisesti kahteen laajempaan luokkaan: täsmähakuihin ja tutkivaan hakuun. Täsmähauissa tiedon etsijällä on alusta lähtien olemassa selkeä hakutavoite, ja tiedon etsintä päättyy, kun hän löytää haluamansa tiedon. Tutkivassa haussa käyttäjä taas keskittyy asioiden oppimiseen ja selvittämiseen ilman tuollaista täsmällistä tavoitetta. Vaikka tutkiva haku on hyvin merkittävä osa käyttäjien toimintaa, se on heille luonnollisesti hyvin haastavaa, koska heillä ei ole entuudestaan riittävästi tietoa tarkasteltavasta aihepiiristä . Toisaalta samaan aikaan heidän tiedontarpeensa ovat laajoja ja monimutkaisia, ja ne myös muuttuvat koko ajan. Käyttäjän avustaminen tutkivassa haussa ei myöskään tiedonhakujärjestelmissä ole helppoa juuri siksi, että käyttäjien tiedontarpeet ovat niin monimuotoisia ja heidän toimintansa niin dynaamista. Lisäksi tutkivan haun täsmällinen määritteleminen käsitteellisesti on hyvin vaikeaa. Sen vuoksi useimmat suositut tiedonhakujärjestelmät soveltuvat hyvin vain täsmähakuihin. Koska tiedon etsintätapoja kuitenkin on niin monenlaisia, tarvitaan selvästi parempia tiedonhakujärjestelmiä, jotka tukevat tiedonhakua eri tavoin. Tämän väitöskirjatyön ensisijaisena tavoitteena on ollut mahdollistaa sellaisten tiedonhakujärjestelmien suunnitteleminen, jotka tukevat käyttäjää yhtä hyvin sekä täsmähauissa että tutkivassa haussa. Tätä ongelmaa lähestyttiin mallintamalla tiedon etsintää rationaalisesti mukautuvana vuorovaikutuksena, joka auttaa määrittelemään käsitteellisesti selkeämmin sekä tutkivan haun että täsmähaut. Tässä työssä, joka pohjautuu mukautuvan vuorovaikutuksen tutkimiseen aiemmin laadittuun kehikkoon, oletettiin, että käyttäjien tapaan olla vuorovaikutuksessa hakujärjestelmien kanssa vaikuttaa kolme keskeistä tekijää: ympäristön ekologinen rakenne, käyttäjien kognitiiviset ja aisteihin liittyvät rajoitukset sekä saadun informaatiohyödyn ja tiedon etsintään käytetyn ajan välisen suhteen optimointi. Väitöskirjatyössä esitetään kolme mukautuvaan vuorovaikutuksen kehikkoon pohjautuvaa mallia, joilla 1) pyritään ennustamaan, miten käyttäjän tiedonhakutarpeet muuttuvat tutkivan haun aikana, 2) erottamaan toisistaan tutkivaan hakuun ja täsmähakuihin liittyvät tehtävät sekä 3) ennustamaan erilaisten mukautuvien hakustrategioiden syntyminen. Työn lopussa esitellään myös tapa, jolla ehdotetut mallit voidaan integroida osaksi tiedonhakujärjestelmää niin, että järjestelmä mukautuu tukemaan käyttäjää sekä täsmähauissa että tutkivassa haussa. Työssä tehdyt havainnot vahvistavat, että tiedon etsintää voidaan mallintaa mukautuvan vuorovaikutuksen avulla. Väitöskirjassa kehitettyjen mallien toimivuutta on arvioitu kokeellisesti käyttäjätutkimuksissa, joissa on käytetty näihin malleihin pohjautuvaa mukautuvaa hakujärjestelmä. Nämä tehdyt tutkimukset myös vahvistavat, että tällainen tiedonhakujärjestelmä voi parantaa tiedonhaun onnistumista, koska se mukautuu dynaamisesti erilaisiin tiedonhakutehtäviin. Väitöskirjan tuloksena onkin lähestymistapa, joka voi osoittautua hedelmälliseksi tavaksi sekä tehostaa että helpottaa tiedonhakua tulevaisuuden tiedonhakujärjestelmissä

Integrated content presentation for multilingual and multimedia information access

For multilingual and multimedia information retrieval from multiple potentially distributed collections generating the output in the form of standard ranked lists may often mean that a user has to explore the contents of many lists before finding sufficient relevant or linguistically accessible material to satisfy their information need. In some situations delivering an integrated multilingual multimedia presentation could enable the user to explore a topic allowing them to select from among a range of available content based on suitably chosen displayed metadata. A presentation of this type has similarities with the outputs of existing adaptive hypermedia systems. However, such systems are generated based on “closed” content with sophisticated user and domain models. Extending them to “open” domain information retrieval applications would raise many issues. We present an outline exploration of what will form a challenging new direction for research in multilingual information access

STROOPWAFEL: Simulating rare outcomes from astrophysical populations, with application to gravitational-wave sources

Gravitational-wave observations of double compact object (DCO) mergers are providing new insights into the physics of massive stars and the evolution of binary systems. Making the most of expected near-future observations for understanding stellar physics will rely on comparisons with binary population synthesis models. However, the vast majority of simulated binaries never produce DCOs, which makes calculating such populations computationally inefficient. We present an importance sampling algorithm, STROOPWAFEL, that improves the computational efficiency of population studies of rare events, by focusing the simulation around regions of the initial parameter space found to produce outputs of interest. We implement the algorithm in the binary population synthesis code COMPAS, and compare the efficiency of our implementation to the standard method of Monte Carlo sampling from the birth probability distributions. STROOPWAFEL finds $\sim$25-200 times more DCO mergers than the standard sampling method with the same simulation size, and so speeds up simulations by up to two orders of magnitude. Finding more DCO mergers automatically maps the parameter space with far higher resolution than when using the traditional sampling. This increase in efficiency also leads to a decrease of a factor $\sim$3-10 in statistical sampling uncertainty for the predictions from the simulations. This is particularly notable for the distribution functions of observable quantities such as the black hole and neutron star chirp mass distribution, including in the tails of the distribution functions where predictions using standard sampling can be dominated by sampling noise.Comment: Accepted. Data and scripts to reproduce main results is publicly available. The code for the STROOPWAFEL algorithm will be made publicly available. Early inquiries can be addressed to the lead autho

Focused browsing: Providing topical feedback for link selection in hypertext browsing

When making decisions about whether to navigate to a linked page, users of standard browsers of hypertextual documents returned by an information retrieval search engine are entirely reliant on the content of the anchortext associated with links and the surrounding text. This information is often insufficient for them to make reliable decisions about whether to open a linked page, and they can find themselves following many links to pages which are not helpful with subsequent return to the previous page. We describe a prototype focusing browsing application which provides feedback on the likely usefulness of each page linked from the current one, and a term cloud preview of the contents of each linked page. Results from an exploratory experiment suggest that users can find this useful in improving their search efficiency

Workshop on web information seeking and interaction

The World Wide Web has provided access to a diverse range of information sources and systems. People engaging with this rich network of information may need to interact with different technologies, interfaces, and information providers in the course of a single search task. These systems may offer different interaction affordances and require users to adapt their informationseeking strategies. Not only is this challenging for users, but it also presents challenges for the designers of interactive systems, who need to make their own system useful and usable to broad user groups. The popularity of Web browsing and Web search engines has given rise to distinct forms of information-seeking behaviour, and new interaction styles, but we do not yet fully understand these or their implications for the development of new systems

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

Synchronous collaborative information retrieval with relevance feedback

Collaboration has been identified as an important aspect in information seeking. People meet to discuss and share ideas and through this interaction an information need is quite often identified. However the process of resolving this information need, through interacting with a search engine and performing a search task, is still an individual activity. We propose an environment which allows users to collaborate to satisfy a shared information need. We discuss ways to divide the search task amongst collaborators and propose the use of relevance feedback, a common information retrieval process, to enable the transfer of knowledge across collaborators during a search session. We describe the process by which co-searchers can collaborate effectively with little redundancy and how we can combine relevance judgements from multiple searchers into a coherent model for synchronous collaborative information retrieva