1. Improving relevance with log analysis Richard Boulton [email_address] @rboulton

2. Sources of ranking information

4. BM25, BM25F

5. Purely based on document and query

7. Citation analysis

9. How well did results which are similar to this result perform in the past.

10. How to analyse historical behaviour

12. Keep click-through information.

13. Keep track of eventual outcomes (sales, ad views, content downloads).

15. Map-combine-reduce

16. Very good for log analysis!

18. Very simple to use.

19. Very poor documentation, sadly.

20. Some performance penalty for using python, but very good for ad-hoc jobs and rapid development.

22. New results were never picked

23. New queries never had results picked

24. Need massive volume to get anywhere

26. Represent documents in terms of features .

27. Use history to produce a score for each result.

28. Use machine learning to build a model to predict the score for a set of features.

29. Use model to produce scores for ranking.

31. Review scores

32. Categories

39. Don't need very much data to build a useful model.

40. * for some sample news data.

42. Tie searches to results in logs

43. Dumbo + Hadoop makes adhoc investigation of behaviour easy.