wiki:GSoC2019/LTR/Work Product

Learning to Rank

Work Product

  • My main goal of this project was to make letor officially releasable and write extensive tests and documentation which I successfully completed.
  • My work involved adding PIMPL implementation of feature class and modified the api design to make some optimizations regarding sharing the Feature::Internal object among all the features in the featurelist object so that maps related to statistics do not get copied for every feature.
  • Wrote xapianletor-config script and related macros.
  • Fixed bugs in
  • Fixed parse_query method and score method in
  • Tested xapian-letor through xapian-evaluation code and fixed bugs in xapian-evaluation and compare the performance of letor with other weighting schemes and also among different rankers.
  • Fixed various bugs in ranker API.
  • Fixed the implementation of rankers and indexing errors in ranker API and xapian-evaluation code.
  • Added Xavier initialization and bias term.
  • Wrote python3 bindings for xapian-letor.


Link of all the PRs
Link of all the merged commits

Work in Progress

PR 270 and 278 are work in progress. PR 270 adds python3 bindings along with python3 examples. PR 278 fixes the implementation of rankers.

Future Work

  • Investigate research papers which might help in improving the performance of letor against BM25+ weighting scheme.
  • Write bindings for languages other than python3.
Last modified 9 months ago Last modified on 08/26/19 07:27:27
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