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Information Retrieval in Context of Digital Libraries - or DL in Context of IR. Peter Ingwersen Royal School of LIS Denmark – firstname.lastname@example.org http://www.db.d/pi. Agenda. Information Retrieval In Context of Information Behavior Laboratory Model = Digital Library approach?
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Information Retrieval in Context of Digital Libraries- or DL in Context of IR Peter Ingwersen Royal School of LIS Denmark – email@example.com http://www.db.d/pi
Agenda • Information Retrieval • In Context of Information Behavior • Laboratory Model = Digital Library approach? • Integrated Model – roles of context • The social perspective • Challenges in IR / DL according to model • Conclusions LIDA 2009
Information Retrieval • The processes involved in the representation, storage, searching, finding, filtering, presentation and use of information relevant to a requirement for information desired by a human user (The Turn, 2005) • Interaction – Time dimension LIDA 2009
Information behaviour and IR T. Wilson´s Onion Model, 1999 - extended: Job-related Work Tasks Interests Non-job-related Tasks and Interests Daily-life behavior Seeking IR Interactive IR Information behaviour Behaviour LIDA 2009
Information behaviour … and other central concepts in Information Studies • Information behaviour: • to create information – e.g., on the Net - blogs; also human indexing, including social tagging; • to produce publications – e.g., as publisher • to communicate – face-to-face; chat; e-mail • to manage information sources – e.g. KM; selectivity LIDA 2009
IB and other central concepts … • Information seeking (behaviour) • Information behaviour with interest for Information • Information needexist – even muddled or exploratory • Searching information sources – e.g. colleagues • Information Retrieval (I)IR • Searching information space via systems – Digital Library & Assets (interactive IR) • Retrieval models; relevance feedback & ranking; query modification; auto indexing and weighting; LIDA 2009
Search request Represen- tation Represen- tation Database Query Matching Query Result The Laboratory Model of IR(in the Cranfield-TREC Laboratory Research Framework) Docu- ments Pseudo Relevance Feedback Could just as well be a model for Digital Library development LIDA 2009
The Lab IR Cave, with a Visitor The Turn – Ingwersen & Järvelin, 2005 Context Docu- ments Search request Represen- tation Represen- tation Database Query Matching Query Result
Information objects Social Tagging Org. R = Request / Relevance feedback Information Seeker(s) Interface Social Interaction Query R Social Context Modification IT: Engines Logics Algorithms Recommender techniques Cultural Short-term IS&R & social interaction Cognitive transformations and influence over time Simplistic model of (I)IR – short-term interaction – in context LIDA 2009
Central Components of Interactive IR – the basic Integrated Framework In situ tagging The Lab./DL Framework In situ recommendation LIDA 2009 Ingwersen
Integrated Framework and Relevance Criteria Socio-organizational& culturalcontext Work task context Seeking context Seeking Task Work Task Docs Request Seeking Process Work Process Repr Repr DB Query Match Result Task Result Seeking Result IR context D: Socio-cognitive relevance; quality of work task result C:Qualitywork process/result; Graded R. Evaluation Criteria: B: Usability, Graded rel., CumGain; Quality of information/process A: Recall, precision, efficiency Ingwersen LIDA 2009
Moving into Context • Strength: • Involvementof TASK (work/search) and … • Processes for fulfillment of task and … • Task result / outcome • Seeking and retrieval tasks influenced by work tasks • Pointing to novel relevance measures • Task fulfillment measures; socio-cognitive relevance; social utility (tagging, visits, downloads …) LIDA 2009
Challenges to IR/DL • “[If] we consider that unlike art IR is not there for its own sake … then IR is far, far more than a branch of computer science” • And what information and relevance means to IR, Tefko Saracevic states (1997, p. 17) … • “[In] broadest sense: Information is … that involves not only messages (first sense) that are cognitively processed (second sense), but also a context – a situation, task, problem-at-hand, the social horizon, … intentions …” LIDA 2009
Challenges to IR/DL – 2 • Understanding actors’ goals, tasks intentions – in diversity of contexts • Job-related knowledge enquiries • Daily-life information explorative behaviors • Entertainment - or simply ‘meaning making’ • Inference of goals, tasks, intentions fromimplicit evidence from interaction behavior • Implicit relevance feedback study examples LIDA 2009
Challenges to IR/DL – 3 • Leading to finding out the best algorithmic models and solutions – not in themselves – but given understanding of characteristics of searcher goals, … • A lot of searching is undirected, vague, random, exploratory, muddled … (Skov, 2009) • A lot of tagging (and folksonomies) is randomly done - but can be filtered LIDA 2009
Challenges to IR/DL – 4Belkin, Nick. Sigir Forum, 42(1), 2008: 47-54 • Recommender systems and personalization are relying on a narrowconception, applying vague correlations between a current searcher’s situation and previous • Dwell time on page; • Click-through • Viewed, rated or saved objects by other searchers • Search profiles’ contents • To tailor the rank of search results • Or to find ‘things alike’ (probably better) LIDA 2009
Challenges to IR/DL – 5 • Which of the (personal) contextual features do we need to involve – incl. the IT context? • How to present retrieved and filtered documents? • Zooming in/out – integrated searching of media & document types: presentation form and relevance/usability: • Are interface issues solved by Google snippets and Microsoft’s detail-whole format? • Alternative (elaborated) evaluation methods for interaction design (IR/DL) are required LIDA 2009
Info. Objects Org. Cognitive Actor(s) (team) Social Context IT Cultural The Circle of Systemic/Social Contexts in interaction design: Digital Libraries & (I)IR – actor as centre IR Interaction Social Interaction Inter- face LIDA 2009
Conclusions • IR and DL (or Digital Assets including museums and cultural heritage) face same challenges of addressing the • Interactive nature of the information process • Contexts – and their limits • Evaluation & research approaches • Need for combined efforts of IT and behavior LIDA 2009
Thank You! LIDA 2009