Information Retrieval in Context of Digital Libraries - or DL in Context of IR Peter Ingwersen Royal School of LIS Denmark –

LIDA Ingwersen 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 Ingwersen 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 Ingwersen Information behaviour and IR T. Wilson´s Onion Model, extended: Seeking IR Job-related Work Tasks Interests Non-job-related Tasks and Interests Daily-life behavior Information behaviour Interactive IR Behaviour

LIDA Ingwersen 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

IB and other central concepts … Information seeking (behaviour) Information behaviour with interest for Information Information need exist – 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 Ingwersen

LIDA Ingwersen The Laboratory Model of IR (in the Cranfield-TREC Laboratory Research Framework) Could just as well be a model for Digital Library development Docu- ments Represen- tation Database Search request Query Matching Represen- tation Query Result Query Result Pseudo Relevance Feedback

The Lab IR Cave, with a Visitor The Turn – Ingwersen & Järvelin, 2005 Docu- ments Represen- tation Database Search request Query Matching Represen- tation Query Result Query Result Context

LIDA Ingwersen Simplistic model of (I)IR – short-term interaction – in context Information objects IT: Engines Logics Algorithms Interface Information Seeker(s) Org. Cultural RQuery R = Request / Relevance feedback Short-term IS&R & social interaction Cognitive transformations and influence over time Modification Social Interaction Social Tagging Recommender techniques Social Context

LIDA Ingwersen Central Components of Interactive IR – the basic Integrated Framework The Lab./DL Framework In situ recommendation In situ tagging

LIDA Ingwersen Integrated Framework and Relevance Criteria Docs Repr DB Request Query Match Repr Result A: Recall, precision, efficiency B: Usability, Graded rel., CumGain; Quality of information/process C: Quality work process/result; Graded R. Work Task Seeking Task Seeking Process Work Process Task Result Seeking Result Evaluation Criteria: Work task context Seeking context IR context Socio-organizational& cultural context D: Socio-cognitive relevance; quality of work task result

LIDA Ingwersen Moving into Context Strength: Involvement of 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 Ingwersen 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 Ingwersen 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 from implicit evidence from interaction behavior Implicit relevance feedback study examples

LIDA Ingwersen 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 Ingwersen Challenges to IR/DL – 4 Belkin, Nick. Sigir Forum, 42(1), 2008: Recommender systems and personalization are relying on a narrow conception, 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 Ingwersen 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 Ingwersen The Circle of Systemic/Social Contexts in interaction design: Digital Libraries & (I)IR – actor as centre Inter- face Cognitive Actor(s) (team) Org. Cultural Social Context Info. Objects IT Social Interactio n IR Interactio n

LIDA Ingwersen 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 Ingwersen Thank You!