Experiences using F# for developing analysis scripts and tools over search engine query log data
Stefan Savev and Peter Bailey
Presented at IFL 2010
This paper describes the strengths and weaknesses of functional programming in F# for analysis of large textual datasets.
A search engine in a few lines.: yes, we can!
Stefan Savev
SIGIR 2009
This paper shows how easy it is to implement a standard research-style search engine in the F# functional programming language.
Document selection methodologies for efficient and effective learning-to-rank
Javed A. Aslam, Evangelos Kanoulas, Virgil Pavlu, Stefan Savev, Emine Yilmaz
SIGIR 2009
This paper evaluates the effect of training data (positive/negative documents) over the quality of the search engine ranking function derived with various machine learning algorithms. This paper finds that current algorithms have problems with imbalanced data and when the diversity between selected documents is small.
Evaluation of phrasal query suggestions
Alan Feuer, Stefan Savev, Javed A. Aslam
CIKM 2007
This paper shows that query suggestions in search engines improves the experience of search engine users. The query suggestions were created from automatically extracted phrases.