Relevance feedback using query-logs Gaurav Pandey Supervisors: Prof. Gerhard Weikum Julia Luxenburger

Motivation Results Search Engine Query “ One size fits all ”

Motivation Results Search Engine User info Query

Motivation Python

Motivation Python CGI code Debugging programming

Usage of Query Logs Clickthrough data Past queries Documents clicked

Usage of Query Logs Query Clicked Documents History Instance

Query Reformulation Result Query: “python information CGI code examples program code debugging bug removal programming” But, p(python/query)=? p(CGI)/query)=? p(code)/query)=? …………………………..

Language Model Normally(without using history), w:term d: document q:query Importance of term w in current query Considers only the current query But, not history instances

Language Model Normally(without using history), w:term d: document q:query Importance of term w in current query Now, using history: Importance of term w in current query + history instances ?

Language Model+History Importance of the term w at one instance in the history Importance of term w in history instances History query: “ CGI code ” Documents: “CGI examples”, “program code ” History query: “ CGI code ” Documents: “CGI examples”, “program code ”

Equal Weighting Works,but can be improved

Discriminative Weighting Choose different for every history instance.. How?

Overlap if a history query has common terms with the current query then λ i = 1, Else if there is no common term λ i =0 Example: Current query “python information” History query:”python code” λ i = 1 History query:”world cup” λ i = 0

Soft overlap if a history query has common terms with the current query then λ i = a, Else if there is no common term λ i =b (a>b) Example: Current query “python information” History query:”python code” λ i = 8 History query:”world cup” λ i = 2

Decrease with time Use uniformly decreasing values If there are n history instances, 1 =n 2 =n-1 3 =n-2 …… n-1 =2 n =1

Decrease with time Use geometrically decreasing values If there are n history instances, 1 =n 2 =n/2 3 =n/3 …… n-1 =n/(n-1) n =1

Experiment Comparison of the  4 techniques  Equal weighting  Basic model (without history) Use similar techniques for:  Probabilistic model  Vector space model

Thanks