1. Information retrieval (IR)
Basics, models, interactions

2. Central ideas
Information retrieval (IR) is at the heart of ALL indexing & abstracting databases, information resources, and search engines
all work on basis of IR algorithms and procedures
Contemporary IR is also interactive – to such a degree that pragmatically IR can not be separated from interaction
As a searcher you will constantly use IR, thus you have to be knowledgeable about it

3. ToC Information retrieval (IR)
Matching algorithms: Exact match & best match
Strength & weaknesses
IR Interaction & interactive models

4. Definitions. Traditional model
1. Information retrieval
Definitions. Traditional model

5. Information retrieval (IR) - original definition
Calvin Mooers ( ) coined the term
“Information retrieval embraces the intellectual aspects of the description of information and its specification for search, and also whatever systems, techniques, or machines are employed to carry out the operation.”
Mooers, 1951

6. IR: Objective & problems
Objectives:
Provide users with effective access to & interaction with information resources.
Retrieve information or information objects that are relevant
Problems addressed:
1. How to organize information intellectually?
2. How to specify search & interaction intellectually?
3. What systems & techniques to use for those processes?

7. IR models
Model depicts, represents what is involved - a choice of features, processes, things for consideration
Several IR models used over time
traditional: oldest, most used, shows basic elements involved
interactive: more realistic, favored now, shows also interactions involved; several models proposed
Each has strengths, weaknesses
We start with traditional model to illustrate many points - from general to specific examples

8. Description of traditional IR model
It has two streams of activities
one is the systems side with processes performed by the system
other is the user side with processes performed by users & intermediaries (you)
these two sides led to “system orientation” & “user orientation”
in system side automatic processing is done; in user side human processing is done
They meet at the matching process
where the query is fed into the system and system looks for documents that match the query
Also feedback is involved so that things change based on results
e.g. query is modified & new matching done

9. Traditional IR model System User Acquisition Problem Representation
documents, objects
Problem
information need
Representation
indexing, ...
Representation
question
File organization
indexed documents
Query
search formulation
Matching
searching
feedback
Retrieved objects

10. Content: What is in databases
Acquisition
system side
Content: What is in databases
In Dialog first part of blue sheets: File Description, Subject Coverage; in Scopus Subject Areas
Selection of documents & other objects from various sources - journals, reports …
In Blue Sheets: Sources; in Scopus Sources
Mostly text based documents
Full texts, titles, abstracts ...
But also: data, statistics, images (e.g. maps, trade marks) ...
Importance: Determines contents of databases Key to file selection in searching !!!

11. Abstracting; summarizing Bibliographic description:
Representation
of documents, objects … system side
Indexing – many ways :
free text terms (even in full texts)
controlled vocabulary - thesaurus
manual & automatic techniques
Abstracting; summarizing
Bibliographic description:
author, title, sources, date…
metadata
Classifying, clustering
Organizing in fields & limits
in Dialog: Basic Index, Additional Index. Limits
in Scopus pull down menus
Basic to what is available for searching & displaying

12. Enables searching & interplay between types of files
File organization
system side
As mentioned:
Sequential
record (document) by record
Inverted
term by term; list of records under each term
Combination: indexes inverted, documents sequential
When citation retrieved only, need for document files or document delivery
Enables searching & interplay between types of files

13. Critical for examination in interview
Problem
user side
Related to user’s task, situation, problem at hand
vary in specificity, clarity
Produces information need
ultimate criterion for effectiveness of retrieval
how well was the need met?
Inf. need for the same problem may change, evolve, shift during the IR process - adjustment in searching
often more than one search for same problem over time
you will experience this in your term project
Critical for examination in interview

14. Determines search specification - a dynamic process
Representation
question – user side
Non-mediated: end user alone
Mediated: intermediary + user
interviews; human-human interaction
Question analysis
selection, elaboration of terms
various tools may be used
thesaurus, classification schemes, dictionaries, textbooks, catalogs …
Focus toward
deriving search terms & logic
selection of files, resources
Subject to feedback changes
Critical roles of intermediary - you
Determines search specification - a dynamic process

15. Translation into systems requirements & limits
Query
search formulation – user side
Translation into systems requirements & limits
start of human-computer interaction
Selection of files, resources
Search strategy - selection of:
search terms & logic
possible fields, delimiters
controlled & uncontrolled vocabulary
variations in tactics
Reiterations from feedback
several feedback types: relevance feedback, magnitude feedback ...
query expansion & modification
What & how of actual searching

16. Various search algorithms: Each has strengths, weaknesses
Matching
searching – system side
Process of comparing
search: what documents in the file match the query as stated?
Various search algorithms:
exact match - Boolean
still available in most, if not all systems
best match - ranking by relevance
increasingly used e.g. on the web
hybrids incorporating both
e.g. Target, Rank in Dialog
Each has strengths, weaknesses
no ‘perfect’ method exists
and probably never will
Involves many types of search interactions & formulations

17. What a user (or you) sees, gets, judges – can be specified
Retrieved objects
from system to user
Various order of output:
sorted by Last In First Out (LIFO)
ranked by relevance & then LIFO
ranked by other characteristics
Various forms of output
In Dialog: Output options
in Scopus title (default), abstract + references, cited by, plus more
When citations only available: possible links to document delivery
Scopus View at publisher
accessing RUL for digital journals
Base for relevance, utility evaluation by users
What a user (or you) sees, gets, judges – can be specified

18. Exact match & best match searches
2. Matching algorithms
Exact match & best match searches

19. Exact match - Boolean search
You retrieve exactly what you ask for in the query:
all documents that have the term(s) with logical connection(s), and possible other restrictions (e.g. to be in titles) as stated in the query
exactly: nothing less, nothing more
Based on matching following rules of Boolean algebra, or algebra of sets
‘new algebra’
presented by circles in Venn diagrams

20. Boolean algebra: operates on sets of documents
A OR B: retrieves set that has either term A or B
often called union and labeled A  B
I want documents that have either term library or term digital someplace within
A NOT B: retrieves set that has term A but not B
often called negation and labeled A – B
I want documents that have term library but if they also have term digital I do not want those
Has four operations (like in algebra):
A: retrieves set that has term A
I want documents that have the term library
A AND B: retrieves set that has terms A and B
often called intersection & labeled A  B
I want documents that have both terms library and digital someplace within

21. Potential problems But beware:
digital AND library will retrieve documents that have digital library (together as a phrase) but also documents that have digital in the first paragraph and library in the third section, 5 pages later, and it does not deal with digital libraries at all
thus in Scopus or Google you will ask for “digital library” and in Dialog for digital(w)library to retrieve the exact phrase digital library
digital NOT library will retrieve documents that have digital and suppress those that along with digital also have library, but sometimes those suppressed may very well be relevant. Thus, NOT is also known as the “dangerous operator “
also beware of order: venetian AND blind will retrieve documents that have venetian blind and also that have blind venetian (oldest joke in information retrieval)

22. Boolean algebra depicted in Venn diagrams
Four basic operations:
e.g. A = digital B= libraries 1 2 3 A B
A alone. All documents that have A. Shade 1 & 2. digital 1 2 3 A B
A AND B. Shade 2
digital AND libraries 1 2 3 A B
A OR B. Shade 1, 2, 3
digital OR libraries 1 2 3 A B
A NOT B. Shade 1
digital NOT libraries

23. Venn diagrams … cont. Complex statements allowed e.g A B 21 2 3 5 6 7
(A OR B) AND C
Shade 4,5,6
(digital OR libraries) AND Rutgers
(A OR B) NOT C
Shade what?
(digital OR libraries) NOT Rutgers 4 C

24. Venn diagrams cont. Complex statements can be made
as in ordinary algebra e.g. (2+3)x4
As in ordinary algebra: watch for parenthesis:
2+(3 x 4) is not the same as (2+3)x4
(A AND B) OR C is not the same as A AND (B OR C)

25. Adding variations to Boolean searches
digital AND libraries can be specified to appear in given fields as present in the given system
e.g. to appear in titles only
in Dialog command is s digital AND libraries/TI
in Scopus pull down menu allows for selection of given field, – so for digital library specify Article Title in pull down menu
in Google Advanced Search gets you to a pull down menu for Where your keywords show up: & then go to in the title of the page
Various systems have different ways to retrieve singular and plurals for the same term
in Scopus term library will retrieve also libraries & vice versa
in Dialog you have to specify librar? to retrieve variants
in Google library retrieves library but not libraries

26. Best match searching Output is ranked
it is NOT presented as a Boolean set but in some rank order
You retrieve documents ranked by how similar (close) they are to a query (as calculated by the system)
similarity assumed as relevance
ranked from highest to lowest relevance to the query
mind you, as considered by the system
you change the query, system changes rank
thus, documents as answers are presented from those that are most likely relevant downwards to less & less likely relevant as determined by a given algortihm
remember: a system algorithm determines relevance ranking

27. Best match ... cont. Best match process deals with PROBABILITY:
what is the probability that a document is relevant to a query?
compares the set of query terms with the sets of terms in documents
calculates a similarity between query & each document based on common terms &/or other aspects
sorts the documents in order of similarity
assumes that the higher ranked documents have a higher probability of being relevant
allows for cut-off at a chosen number e.g. the first 20 documents
BIG issue: What representation & similarity measures are better? Subject of IR experiments
“better” determined by a number of criteria, e.g. relevance, speed …

28. Best match (cont.)
Variety of algorithms (formulas) used to determine similarity
using statistic &/or linguistic properties
e.g. if digital appears a lot of times in a given document relative to its size, that document will be ranked higher when the query is digital
many proposed & tested in IR research
many developed by commercial organizations
Google also uses calculations as to number of links to/from a document & other methods
many algorithms are now proprietary & not disclosed
the way a system ranks and you rank may not necessarily be in agreement
Web outputs are mostly ranked
but Dialog allows ranking as well, with special commands

29. Best vs. exact match Traditional IR model
3. Strengths & weaknesses
Best vs. exact match Traditional IR model

30. Boolean vs. best match Boolean Best match allows for logic
provides all that has been matched
BUT
has no particular order of output – usually LIFO
treats all retrievals equally - from the most to least relevant ones
often requires examination of large outputs
Best match
allows for free terminology
provides for a ranked output
provides for cut-off - any size output
BUT
does not include logic
ranking method (algorithm) not transparent
whose relevance?
where to cut off?

31. Strengths of traditional IR model
© Tefko Saracevic
Strengths of traditional IR model
Lists major components in both system & user branches
Suggests:
What to explain to users about system, if needed
What to ask of users for more effective searching (problem ...)
Aids in selection of component(s) for concentration
mostly ever better representation
Provides a framework for evaluation of (static) aspects

32. IR is a highly interactive process - thus additional model(s) needed
© Tefko Saracevic
Weaknesses
Does not address nor account for interaction & judgment of results by users
identifies interaction with matching only
interaction is a much richer process
Many types of & variables in interaction not reflected
Feedback has many types & functions - also not shown
Evaluation thus one-sided
IR is a highly interactive process - thus additional model(s) needed

33. Models. Implications: what happens in searching?
4. IR interaction
Models. Implications: what happens in searching?

34. Enters interaction
There is MUCH more to searching than knowing computers, networks & commands, as there is more to writing than knowing word processing packages

35. © Tefko Saracevic, Rutgers University
IR as interaction
If we consider USER & USE central, then:
Interaction is a dominant feature of contemporary IR
Interaction has many facets:
with systems, technology
with documents, texts viewed/retrieved
intermediaries with people
Several interactive IR models
none as widely accepted as traditional IR model
Broader area: human-computer interaction (HCI) studies

36. © Tefko Saracevic, Rutgers University
HCI: broader concepts
“Any interaction takes place through one or more interfaces & involves two or more participants who each have one or more purposes for the interaction”
Storrs, 1994
Participants: people & ‘computer’ (everything in it – software, hardware, resources …)
Interface: a common boundary
Purposes: people have purposes and ‘computer’ has purposes built in
At issue: identification of important aspects, roles of each

37. © Tefko Saracevic, Rutgers University
HCI … definitions
“Interaction is the exchange of information between participants where each has the purpose of using the exchange to change the state of itself or of one or more of others”
“An interaction is a dialogue for the purpose of modifying the state of one or more participants”
Key concepts: exchange, change
for user: change the state of knowledge related to a given problem, tasks, situation

38. © Tefko Saracevic, Rutgers University
IR interaction is ...
“... the interactive communication processes that occur during the retrieval of information by involving all the major participants in IR, i.e. the user, the intermediary, and the IR system.” Ingwersen, 1992
Involved:
users
intermediaries (possibly)
everything in IR system
communication processes - exchange of information

39. © Tefko Saracevic, Rutgers University
Questions
What variables are involved in interaction?
models give lists
How do they affect the process? How to control?
experiments, experience, observation give answers
Do given interventions (actions) or communications improve or degrade the process?
e.g. searcher’s (intermediaries or end-users) actions
Can systems be designed so that searcher’s intervention improves performance?

40. © Tefko Saracevic, Rutgers University
Interactive IR models
Several models proposed
none as widely accepted as the traditional IR model
They all try to incorporate
information objects (“texts”):
IR system & setting
interface
intermediary, if present
user’s characteristics
cognitive aspects; task; problem; interest; goal; preferences ...
social environment
variety of processes between them all.

41. User modeling (treated in unit 11, but introduced here to illustrate one of the important aspect of human-human interaction)
Identifying elements about a user that impact interaction, searching, types of retrieval …:
who is the user (e.g. education)
what is the problem, task at hand
what is the need; question
how much s/he knows about it
what will be used for
how much wanted, how fast
what environment is involved
Much more than just analyzing a question posed by user
related to reference interview
Used to select resources, specify search concepts and terms, formulate query, select format and amount of results provided, follow up with feedback and reiteration, change tactics …

42. Three interactive models
Three differing models are presented here, each concentrates on a different thing:
Ingwersen concentrates on enumeration of general elements that enter in interaction
Belkin on different processes that are involved as interaction progresses through time
Saracevic on strata or levels of interaction elements on computer and user side
As mentioned, no one interaction model is widely accepted as the traditional IR model

43. Ingwersen’s interactive cognitive model
Among the first to view IR differently from traditional model
Included IR as a system but concentrates also on elements outside system that interact
inf. objects – documents, images …
intermediary – you - & interface
user cognitive aspects
user & general environment
path of request (we call question)
from environment (problem) to query
path of cognitive changes
path of communication
various other paths of interactions

44. Ingwersen’s model graphically
Information
objects
Interface/
Intermediary
Query
User’s
cognitive
Request
Environ
ment
IR system
setting
Cognitive
transformations
Interactive
communication -
space

45. Belkin’s episodes model
Concentrates on what happen in interaction as process
Ingwerson concentrated on elements
Viewed interaction as a series of episodes where a number of different things happen over time
depending on user’s goals, tasks
there is judgment, use, interpretation…
processes of navigation, comparison, summarization …
involving different aspects of information & inf. objects
While interacting we do diverse things, perform various tasks, & involve different objects
Think: what do you do while searching?

46. Belkin’s episodes model
USER
USER CO
USER
Goals
tasks
..... CO
COMPARISON
REPRESENTATION
INTERACTION
Judgment, use,
interpretation,
modification
SUMMARIZATION NA NA
INFOR-
MATION
Type,
medium
mode
level
NAVIGATION
VISUALIZATION
Time

47. Saracevic’ stratified model
Interaction: considers it as a sequence of processes/episodes occurring in several levels or strata*
Interaction = INTREPLAY between levels
Structure:
Several User levels
Produce a Query – it has characteristics
Several Computer levels
They all meet on the Surface level
Dialogue enabled by Interface
user utterances
computer ‘utterances’
Adaptation/changes in all
Geared toward Information use
The IR interaction is then a dialogue between the participants - user and computer - through an interface, with the main purpose to affect the cognitive state of the user for effective use of information in connection with an application at hand. The dialogue can be reiterative, incorporating among others various feedback types, and can exhibit a number of patterns - all of which are topics for study.
The major elements in the stratified model are users and computer, each with a host of variables of their own, having a discourse through an interface. The interface instantiates a variety of interactions, but it is not the focus of interactions, despite that it can in its own right effectively support or frustrate other interactions. We can think of interaction as a sequence of processes occurring in several connected levels or strata. Each strata/level involves different elements and/or specific processes. On the human side processes may be physiological (e.g. visual, tactile, auditory), psychological, and cognitive.

48. Saracevic’s stratification model
Situational
tasks; work context...
Adaptation
Engineering
hardware; connections...
INTERACTION STRATA (levels)
Surface level
Use of information
Query
characteristics …
COMPUTER
Affective
intent; motivation ...
Cognitive
knowledge; structure...
Processing
software; algorithms …
Content
inf. objects; representations...
USER
INTERFACE
Context
social, cultural …

49. Roles of levels or strata
© Tefko Saracevic, Rutgers University
Roles of levels or strata
Defining of what’s involved
whassup?
Help in recognition/separation of differing variables
each strata or level involves different elements, roles, & processes
Observation of interaction between strata - complex dynamics
On the user side suggests what affects factors query and judgment of responses
thus elements for user modeling
The user side has a number of levels. I suggest three to start with: Cognitive, Affective, and Situational:
On the Cognitive level users interact with texts and their representations in the information resources considering them as cognitive structures. Users interpret and judge cognitively the texts obtained, and may assimilate them cognitively.
On the Affective level users interact with their intentions, and all that go with intentionality, such as beliefs, motivation, feelings (e.g. frustration), desires (e.g. for a given degree of completeness), urgency, and so on. Intentionality may be a critical aspect governing all the other user variables.
On the situational level users interact with the given situation or problem-at-hand which produced the information need and resulting question. The results of the search may be applied to the resolution or partial resolution of a problem.
However, things are not that simple. The situation that was the reason for interaction to start with, produced a problem that sometimes may be well sometimes ill defined, and the related question, if not on paper then in user’s mind, may also be defined in various well-ill degrees. In addition, a user also brings a given knowledge or cognitive state related to the situation, as well as an intentionality - these also may be well or ill defined. All this is used on the surface level to specify and modify queries, select files, search terms, search tactics, and other attributes to use in searching and decision-making, and make or change relevance inferences and other decisions.

50. Interplay between levels
© Tefko Saracevic, Rutgers University
Interplay between levels
Interplay on user side:
Cognitive: between cognitive structures of texts & users
Affective: between intentions & other
Situational: between texts & tasks
Similar interplay on computer side
Surface level - interface:
searching, navigation, browsing, display, visualization, query characterization …
Interplay judgments in searching:
evaluation of results - relevance
changing of models: situation, need ...
selection of search terms
resulting modifications - feedback
As the interaction proceeds, a series of dynamic adaptations occur in both elements, user and computer, concentrating toward the surface level, the point where they meet. However, I assume that the use of information proceeds toward application, that is toward the situational level. Adaptations may also signify changes or shifts in a variety of these levels. Various types of feedback play a critical role in various types of adaptation and change. Of great interest is to study the nature, manifestations, and effects of these changes and shifts. Shifts, relative little explored events, are probably among the most important ones that occur in interaction.
Intuitively, we understand that we are doing different things for different purposes while interacting with an IR system. Those who deal with design and other aspects of computer side concentrate at different times on very different computer levels. The stratified model deliberately decomposes the many elements that enter in different types of interaction. In that sense the model is related to the idea of different kinds of interactions on the human side, as suggested by Belkin, and different types of ‘things’ and processes involved on the computer side, as suggested in the traditional IR model. It tries to incorporate both sides in the interaction.

51. © Tefko Saracevic, Rutgers University
Intermediaries - YOU
Intermediaries could participate as an additional interface - many roles:
diagnostic help in problem, query formulation
system interface handling
selection, interpretation & manipulation of inf. resources
interpretation of results
education of users
enablers of end-users
Basic role: optimizing results
Act in processes at different levels

52. Implications
Interaction central to IR including in searching of the Web
We see it on the surface level
But result of MANY variables, levels & their interplay
IR interaction requires knowledge of these levels & interplays
many users have difficulties
so do many professionals
Design of interfaces for interaction still lacking
People compensate in many ways including trial & error, failures

53. What happens in searching?
Highly reiterative process
back & forth between user modeling & (re)formulating search strategy
goes on & on in many feedback loops, twists & turns, shifts
Search strategy (the big picture)
selection/reselection of sources
stating a query (search statement) from a question
terms, their expansions, logic, qualifications, limitations

54. Searching … (cont.) Search tactics (action steps)
what to do first, next
e.g. from broad to narrow searches
format of results
Evaluation of results
as to magnitude - how much?
as to relevance - how well?
feedback to change after that
user model - e.g. question
strategy - e.g. files, query
tactics - e.g. narrowing, broadening

55. Practical suggestions for searchers (filched from a source I cannot find anymore)
Prepare carefully
Understand your opponent -
e.g. Dialog, Scopus, LexisNexis
Anticipate
e.g. hidden meaning of terms
Have a contingency plan
assessing odds of success or points of diminishing returns
Avoid ambiguity
inherent in language
Stay loose!

56. Stay loose?
I copied that, but always wandered what does it really mean?
Dictionary says:
not firmly fastened or fixed in place
???? well, sounds OK! or