© Tefko Saracevic, Rutgers University1 Search strategy & tactics Governed by effectiveness & feedback.

Slides:



Advertisements
Similar presentations
© Tefko Saracevic1 All you wanted to know … Advanced searching.
Advertisements

Evaluating Search Engine
Search Engines and Information Retrieval
© Tefko Saracevic, Rutgers University1 digital libraries and human information behavior Tefko Saracevic, Ph.D. School of Communication, Information and.
© Tefko Saracevic1 Interaction in information retrieval There is MUCH more to searching than knowing computers, networks & commands, as there is more.
Tefko Saracevic1 EVALUATION in searching Requirements Criteria
© Tefko Saracevic, Rutgers University adapted for sectoin 21 PRINCIPLES OF SEARCHING 17:610:530 (02) Paul Kantor SCILS, Rm. 307 (732) /Ext
1 Advanced searching a variety tricks of the trade Tefko Saracevic
© Tefko Saracevic, Rutgers University1 1.Discussion 2.Information retrieval (IR) model (the traditional models). 3. The review of the readings. Announcement.

© Tefko Saracevic, Rutgers University1 Interaction in information retrieval There is MUCH more to searching than knowing computers, networks & commands,
Information Retrieval Concerned with the: Representation of Storage of Organization of, and Access to Information items.
© Tefko Saracevic1 Mediation in librarianship & information retrieval Reference interview Searchers as intermediaries in information retrieval.
Information retrieval (IR)
INFO 624 Week 3 Retrieval System Evaluation
© Tefko Saracevic, Rutgers University 1 EVALUATION in searching IR systems Digital libraries Reference sources Web sources.
© Tefko Saracevic, Rutgers University1 digital libraries and human information behavior Tefko Saracevic, Ph.D. School of Communication, Information and.
© Tefko Saracevic1 Search strategy & tactics Governed by effectiveness&feedback.
Basics Computer Internet Search Strategy. Computer Basics IP address: Internet Protocol Address An identifier for a computer or device on a network The.
17:610:551:01 Where Should the Person Stop and the Information Search Interface Start? Marcia Bates Presented by Albena Stoyanova-Tzankova March 2004.
1 Vocabulary & languages in indexing & searching Connection: indexing searching
© Tefko Saracevic, Rutgers University1 PRINCIPLES OF SEARCHING 17:610:530 (01) Tefko Saracevic SCILS, Rm. 306 (732) /Ext. 8222
© Tefko Saracevic, Rutgers University1 Presentation of search results A search is not finished with the search Guidelines for deliverables.
Tefko Saracevic, Rutgers University 1 Practice for logical operators Boolean search statements and Venn diagrams.
1 Search strategy & tactics Governed by effectiveness & feedback Tefko Saracevic
© Tefko Saracevic, Rutgers University1 digital libraries and human information behavior Tefko Saracevic, Ph.D. School of Communication, Information and.
Evaluation of Evaluation in Information Retrieval - Tefko Saracevic Historical Approach to IR Evaluation.
© Tefko Saracevic 1 Information retrieval (IR): traditional model 1.Why? Rationale for the module. Definition of IR 2.System & user components 3.Exact.
Vocabulary & languages in searching
© Tefko Saracevic, Rutgers University1 Mediation in librarianship & information retrieval Reference interview Human-human interaction Question negotiation.
Query Relevance Feedback and Ontologies How to Make Queries Better.
Search Engines and Information Retrieval Chapter 1.
Evaluation Experiments and Experience from the Perspective of Interactive Information Retrieval Ross Wilkinson Mingfang Wu ICT Centre CSIRO, Australia.
The Cognitive Perspective in Information Science Research Anthony Hughes Kristina Spurgin.
1 © Tefko Saracevic, Rutgers University Evaluation of library and information services (LIS): an overview Contexts Approaches Levels Requirements Measures.
Modern Information Retrieval Computer engineering department Fall 2005.
Evaluation INST 734 Module 5 Doug Oard. Agenda Evaluation fundamentals  Test collections: evaluating sets Test collections: evaluating rankings Interleaving.
Information Retrieval Evaluation and the Retrieval Process.
1 Information Retrieval Acknowledgements: Dr Mounia Lalmas (QMW) Dr Joemon Jose (Glasgow)
Information in the Digital Environment Information Seeking Models Dr. Dania Bilal IS 530 Spring 2006.
Graphical User Interface (GUI) Web site Team Matix Proposal GC 215: Web Publishing.
Search Engine Architecture
Shelly Warwick, MLS, Ph.D – Permission is granted to reproduce and edit this work for non-commercial educational use as long as attribution is provided.
Information in the Digital Environment Information Seeking Models Dr. Dania Bilal IS 530 Spring 2005.
Chapter 8 Evaluating Search Engine. Evaluation n Evaluation is key to building effective and efficient search engines  Measurement usually carried out.
Basic Implementation and Evaluations Aj. Khuanlux MitsophonsiriCS.426 INFORMATION RETRIEVAL.
Information Retrieval in Context of Digital Libraries - or DL in Context of IR Peter Ingwersen Royal School of LIS Denmark –
Basic Online Searching Evaluating and improving search results Session 12. Péter Jacsó Péter Jacsó LIS 663 Fall 2015.
Jane Reid, AMSc IRIC, QMUL, 30/10/01 1 Information seeking Information-seeking models Search strategies Search tactics.
Information Retrieval CSE 8337 Spring 2007 Introduction/Overview Some Material for these slides obtained from: Modern Information Retrieval by Ricardo.
Tefko Saracevic1 Mediation and user modeling “Knowledge is of two kinds. We know a subject ourselves, or we know where we can find information upon it.”
Performance Measures. Why to Conduct Performance Evaluation? 2 n Evaluation is the key to building effective & efficient IR (information retrieval) systems.
Information Retrieval
Information Retrieval Transfer Cycle Dania Bilal IS 530 Fall 2007.
Jhu-hlt-2004 © n.j. belkin 1 Information Retrieval: A Quick Overview Nicholas J. Belkin
L&I SCI 110: Information science and information theory Instructor: Xiangming(Simon) Mu Sept. 9, 2004.
1 IFLA FRBR & MIC Metadata Evaluation Ying Zhang Yuelin Li October 14, 2003.
Generating Query Substitutions Alicia Wood. What is the problem to be solved?
Chapter. 3: Retrieval Evaluation 1/2/2016Dr. Almetwally Mostafa 1.
Evaluation. The major goal of IR is to search document relevant to a user query. The evaluation of the performance of IR systems relies on the notion.
Investigate Plan Design Create Evaluate (Test it to objective evaluation at each stage of the design cycle) state – describe - explain the problem some.
Information Retrieval in Practice
1. Expand 2. Business search 3. Dialindex search
Search Techniques and Advanced tools for Researchers
Modern Information Retrieval
IR Theory: Evaluation Methods
Evaluation of IR Performance
digital libraries and human information behavior
Retrieval Performance Evaluation - Measures
Presentation transcript:

© Tefko Saracevic, Rutgers University1 Search strategy & tactics Governed by effectiveness & feedback

© Tefko Saracevic, Rutgers University2 Some definitions Search statement (query): –set of search terms with logical connectors and attributes - file and system dependent Search strategy (big picture): –overall approach to searching of a question selection of systems, files, search statements & tactics, sequence, output formats; cost, time aspects Search tactics (action choices): –choices & variations in search statements terms, connectors, attributes

© Tefko Saracevic, Rutgers University3 Some definitions (cont.) Cycle : –set of commands from start (begin) to viewing (type) results, or from viewing to viewing command Move : –modifications of search strategies or tactics that are aimed at improving the results

© Tefko Saracevic, Rutgers University4 Some definitions (cont.) Effectiveness : –performance as to objectives to what degree did a search accomplish what desired? how well done in terms of relevance? Efficiency : –performance as to costs at what cost and/or effort, time? Both KEY concepts & criteria for selection of strategy, tactics & evaluation

© Tefko Saracevic, Rutgers University5 Effectiveness criteria Search tactics chosen & changed following some criteria of accomplishment –none - no thought given –relevance –magnitude –output attributes –topic/strategy Tactics altered interactively –role & types of feedback Knowing what tactics may produce what results –key to professional searcher

© Tefko Saracevic, Rutgers University6 Relevance: key concept in IR Attribute/criterion reflecting effectiveness of exchange of inf. between people (users) & IR systems in communication contacts, based on valuation by people Some attributes: –in IR - user dependent –multidimensional or faceted –dynamic –measurable - somewhat –intuitively well understood

© Tefko Saracevic, Rutgers University7 Types of relevance Several types considered: –Systems or algorithmic relevance relation between between a query as entered and objects in the file of a system as retrieved or failed to be retrieved by a given procedure or algorithm. Comparative effectiveness. –Topical or subject relevance: relation between topic in the query & topic covered by the retrieved objects, or objects in the file(s) of the system, or even in existence; Aboutness..

© Tefko Saracevic, Rutgers University8 Types of relevance (cont.) –Cognitive relevance or pertinence: relation between state of knowledge & cognitive inf. need of a user and the objects provided or in the file(s). Informativeness, novelty... – Motivational or affective relevance relation between intents, goals & motivations of a user & objects retrieved by a system or in the file, or even in existence. Satisfaction... –Situational relevance or utility: relation between the task or problem- at-hand. and the objects retrieved (or in the files). Relates to usefulness in decision-making, reduction of uncertainty...

© Tefko Saracevic, Rutgers University9 Effectiveness measures Precision: – probability that given that an object is retrieved it is relevant, or the ratio of relevant items retrieved to all items retrieved Recall: – probability that given that an object is relevant it is retrieved, or the ratio of relevant items retrieved to all relevant items in a file Precision easy to establish, recall is not –union of retrievals as a “trick” to establish recall

© Tefko Saracevic, Rutgers University10 Calculation Precision = a a + b Recall = a a + c High precision = maximize a, minimize b High recall = maximize a, minimize c

© Tefko Saracevic, Rutgers University11 Precision-recall trade-off USUALLY: precision & recall are inversely related –higher recall usually lower precision & vice versa 100 % 0 Ideal Usual Improvements Precision Recall

© Tefko Saracevic, Rutgers University12 Search tactics What variations possible? –Several ‘things’ in a query can be selected or changed that affect effectiveness: 1. LOGIC –choice of connectors among terms (AND, OR, NOT, W …) 2. SCOPE –no. of concepts linked - ANDs (A AND B vs A AND B AND C) 3.EXHAUSTIVITY –for each concept no. of related terms - OR connections (A OR B vs. A OR B OR C)

© Tefko Saracevic, Rutgers University13 Search tactics (cont.) 4. TERM SPECIFICITY –for each concept level in hierarchy (broader vs narrower terms) 5. SEARCHABLE FIELDS –choice for text terms & non-text attributes (titles only, limits) 6. FILE OR SYSTEM SPECIFIC CAPABILITIES (ranking, target, sorting)

© Tefko Saracevic, Rutgers University14 Effectiveness “laws” SCOPE –more ANDs EXHAUSTIVITY –more ORs USE OF NOTs BROAD TERM USE –low specificity Output size: down Recall: down Precision: up Output size: up Recall: up Precision: down Output size down Recall: down Precision: up Output size: up Recall: up Precision: down Output size: down Recall: down Precision: up PHRASE USE - high specificity

© Tefko Saracevic, Rutgers University15 Recall, precision devices BROADENING - higher recall: Fewer ANDs More ORs Fewer NOTs More free text Fewer controlled More synonyms Broader terms Less specific More truncation Fewer qualifiers Fewer LIMITs Citation growing NARROWING - higher precision: More ANDs Fewer ORs More NOTs Less free text More controlled Less synonyms Narrower terms More specific Less truncation More qualifiers More LIMITs Building blocks

© Tefko Saracevic, Rutgers University16 Examples from a study 40 users; question each 4 intermediaries; triadic HCI regular setting videotaped, logged 48 hrs of tape (72 min. avrg) –presearch: 16 min avrg. –online: 56 min avrg. User judgments: 6225 items –3565 relevant or part. rel. –2660 not relevant Many variables, measures & analyses

© Tefko Saracevic, Rutgers University17 Feedback Relevance feedback loops: Content relevance feedback –judging relevance of items Term relevance feedback –looking for new terms Magnitude feedback –number of postings Strategy feedback loops: Tactical review feedback –review of strategy (DS) Terminology review feedback –term review & evaluation

© Tefko Saracevic, Rutgers University18 Data on feedback types Total feedback loops Content rel. fdb. Term rel. fdb. Magnitude fdb. Tactic. rev. fdb. Termin. rev. fdb Feedbacks initiated by: User Intermediary 885 (in 40 questions) R ank 354 (40%) 2 67 (8%) (45%) 1 56 (6%) 4 12 (1%) (40%) 534 (60%) (mostly magnitude)

© Tefko Saracevic, Rutgers University19 DIALOG commands Total number: By type: Select Type Change db. Display sets Limit Expand 1677 (in 40 questions) In no. of quest (63%) (28%) (4%) (3%) (1%) 11 6 (1%) 6