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Relevance Feedback. User tells system whether returned/disseminated documents are relevant to query/information need or not Feedback: usually positive sometimes negative always incomplete Hypothesis: relevant docs should be more like each other than like non-relevant docs.

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Presentation Transcript
relevance feedback
Relevance Feedback
  • User tells system whether returned/disseminated documents are relevant to query/information need or not
  • Feedback:
    • usually positive
    • sometimes negative
    • always incomplete
  • Hypothesis: relevant docs should be more like each other than like non-relevant docs
relevance feedback purpose
Relevance Feedback: Purpose
  • Augment keyword retrieval: Query Reformulation
    • give user opportunity to refine their query
    • tailored to individual
    • exemplar based – different type of information from the query
    • Iterative, subjective improvement
  • Evaluation!
relevance feedback examples
Relevance Feedback: Examples
  • Image Retrieval
    • http://www.cs.bu.edu/groups/ivc/ImageRover/
    • http://nayana.ece.ucsb.edu/imsearch/imsearch.html
    • http://www.mmdb.ece.ucsb.edu/~demo/corelacm/
relevance feedback early usage by rocchio
Relevance Feedback: Early Usage by Rocchio
  • Modify original keyword query
    • strengthen terms in relevant docs
    • weaken terms in non-relevant docs
    • modify original query by weighting based on amount of feedback
relevance feedback early results
Relevance Feedback: Early Results
  • Evaluation:
    • how much feedback needed
    • how did recall/precision change
  • Conclusion:
    • improved recall & precision over even 1 iteration and return of up to 20 non-relevant docs
    • Promising technique
query reformulation
Query Reformulation
  • User does not know enough about document set to construct optimal query initially.
  • Querying is iterative learning process repeating two steps:
    • expand original query with new terms (query expansion)
    • assign weights to the query terms (term reweighting)
query reformulation approaches
Query Reformulation Approaches
  • Relevance feedback based
    • vector model (Rocchio …)
    • probabilistic model (Robertson & Sparck Jones, Croft…)
  • Cluster based
    • Local analysis: derive information from retrieved document set
    • Global analysis: derive information from corpus
vector based reformulation
Vector Based Reformulation
  • Rocchio (~1965)with adjustable weights
  • Ide Dec Hi (~1968) counts only the most similar non-relevant document
probabilistic reformulation
Probabilistic Reformulation
  • Recall from earlier:
  • still need to estimate probabilities:
    • do so using relevance feedback!
estimating probabilities by accumulating statistics
Estimating Probabilities by Accumulating Statistics
  • Dr is set of relevant docs
  • Dr,i is set of relevant docs with term ki
  • ni is number of docs in corpus containing term ki
computing similarity term reweighting
Computing Similarity (Term Reweighting)
  • assume: term independence and binary document indexing
  • Cons: no term weighting, no query expansion, ignores previous weights
croft extensions
Croft Extensions
  • include within document frequency weights
  • initial search variant

Last term is normalized within-document frequency.

C and K are adjustable parameters.

query reformulation summary so far
Query Reformulation: Summary so far…
  • Relevance feedback can produce dramatic improvements.
  • However, must be careful that previously judged documents are not part of “improvement” and techniques have limitations.
  • Next round of improvements requires clustering…
croft feedback searches
Croft Feedback Searches
  • Use probability updates as in Robertson
assumptions
Assumptions
  • Initial query was a good approximation.
  • Ideal query is approximated by shared terms in relevant documents.
assumptions16
Assumptions
  • Initial query was a good approximation.
    • polysemy? synonyms?
    • slang? concept drift?
  • Ideal query is approximated by shared terms in relevant documents.
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