Week 12 - Exam hints part 1
Information retrieval
Boolean model
Vector model

-> Weighting schemes
Tf-idf
BM25 / Pivot Normalisation
Take stuff into account - local evidence, normalisation, global (idf), document lenght normalisation

No need to memorise the equations - explain the main components then you're g
Lookiong at it from an axiomatic approach
other models
Neural networks
extended boolean


Evaluation
Evaluationg different weighting schemes
precision recall
precision recall graph
MAP
user ones - coverage & novelty (only when we have users that we can study or discuss)
purity etc.

Learning
Looked at evolutionary computation
Looked at clustering

give us ways to generate new weighting schemes
K -means

Collab filtering
interesting in evaluating it with coverage and accuracy
websearch
HITS algorithm
Page rank


Query expansion

Query difficulty prediction
how can we tell if a certain query is easy or hard
preretrieval
query features
features of collection

postretreival]
answer set
or even just ranking of documents when they come back


Preprocessing & indexing
system efficiently 

PAPER OVERVIEW
4 questions, do any 3
3 parts to every question

Q1
Weighting schemes
stuff on axiomatic approach (only formal correct way to look at weighting schemes)
tf - idf -> takes issues on overcoming those
vector space model (And other approaches)

Q2
Relevance feedback
know when to do it
what features to use
explicit feedback
similar to assignment - query expansion (but different obviously lmao)

Q3
Alternative model to vector space or boolean 
How we evaluate a system here
anything to do to apply learning algorithms

Q4
similarity - overlaps with assignment questions
recommendation approaches
clustering approaches


Q1. Weighing Schemes
- tf-idf -> dealing with issues with some of the stuff in vector space model, tf-idf
- axiometric approach
-  can determine if a weighting scheme is good or not
- can also be used to constrain a search


Q.2 Relevance feedback
- often want to know when we can use it and features to use
- broken, blind feedback, query, increase precision, soemtimes can damage precision
- want to do query feedback when it is a hard query
- what features should exist if we want feedback
- when user says a doc is relevant or not/ give a score in some range
- query expansion

Q3. Alternatives to Vector Space
- evaluation - precision, recall, preicision recall graphs
- apply learning 


Q4
- similarity - borrow ideas from more than one component oin the course
- clustering approaches
- recommendation approaches