Web search engines

Relevance feedback (contd.)

• Pseudo-relevance feedback
• D+ and D- generated automatically
• E.g.: Cornell SMART system
• top 10 documents reported by the first round of query execution are included in D+
• typically set to 0; D- not used
• Not a commonly available feature
• Web users want instant gratification
• System complexity
• Executing the second round query slower and expensive for major search engines

Ranking by odds ratio

• R : Boolean random variable which represents the relevance of document d w.r.t. query q.
• Ranking documents by their odds ratio for relevance
• .
• Approximating probability of d by product of the probabilities of individual terms in d
• .
• Approximately…

Bayesian Inferencing

• Bayesian inference network for relevance ranking. A document is relevant to the extent that setting its corresponding belief node to true lets us assign a high degree of belief in the node corresponding to the query.

• Manual specification of mappings between terms to approximate concepts.

Bayesian Inferencing (contd.)

• Four layers
• Document layer
• Representation layer
• Query concept layer
• Query
• Each node is associated with a random Boolean variable, reflecting belief
• Directed arcs signify that the belief of a node is a function of the belief of its immediate parents (and so on..)

Bayesian Inferencing systems

• 2 & 3 same for basic vector-space IR systems
• Verity's Search97
• Allows administrators and users to define hierarchies of concepts in files
• Estimation of relevance of a document d w.r.t. the query q
• Set the belief of the corresponding node to 1
• Set all other document beliefs to 0
• Compute the belief of the query
• Rank documents in decreasing order of belief that they induce in the query