07 Feedback Flashcards

1
Q

what are the information needs

A

the underlying cause of the query that a person submit to the search engine
- can be categorised into type of information or type of task

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

how is query related to information need

A

queries is a representation off very different information needs
- but it can be a poor representation as users find it difficult to express
- encouraged to enter short queries
- ambiguous: the same query may represent different needs

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

what is the query formulation problem

A

difficult to generate well formulated queries without
- knowledge of collection
- knowledge of retrieval environments

can learn which one is relevant, first query is trial run

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

what is the key aspect of effective retrieval

A

users cant change the ranking algorithm but can change the result through interaction. IR is an iterative process

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

what is the ASK hypothesis

A

you don’t know what you don’t know:
you can’t search for it if you don’t know about it, therefore there is a need for search engines to show users a list of relevant documents

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

what are some examples of explicit interaction

A
  1. relevance feedback
    - allow user to provide feedback, add terms from relevant document into query
    - use feedback information to reformulate query and produce result
    - allow more interactive process
  2. query expansion
  3. query term suggestion
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

how does relevance feedback using vectors work

A
  1. document that are relevant to one another resemble one another in similar vectors
  2. move query closer towards those vectors by
    - adding vectors for relevant documents to the query vector
    - subtract vectors for the non-relevant documents to the query vector

variations
- add positive weights to relevant terms
- add negative weights to words found in non-relevant document
- remove terms that only appear in non-relevant document

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

relevance feedback performance

A

positive feedback is more valuable than negative feedback
penalise, not remove

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

why is relevance feedback seldom used

A

users generally reluctant to provide explicit feedback
long queries

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

types of user feedback

A

explicit
- relevance feedback
- query suggestion
- similar pages

implicit
- click tracking
- mouse tracking
- user behaviour

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

pseudo relevance feedback

A

assume the top retrieved documents are relevant and do rocchio method/ automatic query expansion
need to reweight the query terms
might drift away from optimal query
eg. homepage of glasgow information retrieval has only 1 unique document, not useful to expand query

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

thesaurus based query expansion

A

for each term in query, expand with related words and synonyms
weight the added terms less than original

How well did you know this?
1
Not at all
2
3
4
5
Perfectly