Results for Exercise Sheet 2 (Ranking)
Add your row to the table below, following the examples already there. Please write down all values with exactly two digits of precision. In the first column, write your first name, or name1+name2 if you work in a group.
Name |
P@3 |
P@R |
MAP |
BM25 Parameters |
Refinements |
Baseline |
0.57 |
0.38 |
0.37 |
k=1.75, b=0.0 |
None (baseline) |
Robin |
0.57 |
0.41 |
0.35 |
k=1.25, b=0.75 |
prefer exact matches and popular documents |
Marco |
0.60 |
0.42 |
0.39 |
k=0.75, b=0.25 |
prefer popular documents (in a very primitive way) |
Julian |
0.60 |
0.43 |
0.40 |
k=0.75, b=0.0 |
prefer popular documents |
Michael+Max |
0.60 |
0.46 |
0.39 |
k=1.42, b=0.21 |
prefer "exact" matches and popular documents |
Lord Voldemort |
0.63 |
0.44 |
0.40 |
k=1.25, b=0.0 |
Dark Magic |
T-Izzle |
0.57 |
0.37 |
0.40 |
k=0.75, b=0.0 |
Boost "all word hits" and hits in Title |
Frank |
0.57 |
0.47 |
0.39 |
k=1.75, b=0.5 |
Prefer popular documents |
Zhang |
0.63 |
0.43 |
0.46 |
k=1.2, b=0.0 |
Prefer exact matches and documents with low tf words |
Hardware Hans-Peter |
0.57 |
0.39 |
0.38 |
k=1.5, b=0.0 |
Prefer popular documents |
Lötkolben Ludwig |
0.63 |
0.42 |
0.38 |
k=1.0, b=0.2 |
Boosted popular documents |
MS-DOS Manfred |
0.60 |
0.40 |
0.38 |
k=1.5, b=0.2 |
Used MS-DOS for development |
Natalie |
0.67 |
0.46 |
0.41 |
k=0.8, b=0.4 |
prefer popular documents and exact matches |
Janosch |
0.53 |
0.41 |
0.37 |
k=0.75, b=0.25 |
*dust* |
Thomas+Frank |
0.57 |
0.42 |
0.38 |
k=1.5, b=0.25 |
prefer popular documents |
Lukas |
0.63 |
0.44 |
0.39 |
k=1.5, b=0.4 |
prefer popular documents and boost multiple hit query words |
Anup |
0.60 |
0.42 |
0.39 |
k=0.6, b=0.0 |
just parameters (k and b) manipulation |
Alex |
0.60 |
0.42 |
0.41 |
k=1.75, b=0.25 |
prefere popular documents and reward the occurence of all query-words |
Dou Nut |
0.40 |
0.02 |
0.03 |
k=1.00, b=0.65 |
Special ranking mechanism implemented |
IE |
0.43 |
0.23 |
0.23 |
k=1.5, b=0.7 |
An attempt wars worth (thanks google translate!) prefer popular docs |
Raghu |
0.37 |
0.23 |
0.20 |
k=1.75, b=0.75 |
Tried multiplicative merging |
Sven |
0.43 |
0.34 |
0.29 |
k=0.75, b=0.25 |
prefer some of the popular documents |
BIOS Bernard |
0.58 |
0.43 |
0.40 |
k=1.2, b=0.0 |
prefer popular movies |
Evgeny+Numair |
0.57 |
0.44 |
0.40 |
k=0.75, b=0.0 |
prefer popular documents |
Jan |
0.24 |
0.11 |
0.10 |
k=1.2, b=0.75 |
baseline |
Kecen |
0.57 |
0.34 |
0.35 |
k=1.25, b=0.2 |
prefer popular records, added weight for keys occur in title |
Elias+Maximilian |
0.5 |
0.31 |
0.3 |
k=1.4, b=0.04 |
prefer popular records, boost if multiple search terms hit |
Matthias |
0.5 |
0.07 |
0.34 |
k=1.23, b=0.53 |
best neighbour optimizer + (far to small) training-set + popularity ranking |
Daniel |
0.3 |
0.14 |
0.13 |
k=1.75, b=0.4 |
Only parameter adjustment |
David |
0.6 |
0.38 |
0.37 |
k=1.75, b=0.0 |
More point for containing all words |
Fabio |
0.48 |
0.41 |
0.35 |
k=0.8, b=0.4 |
best parameters to queries that i used |
Patrick |
0.6 |
0.39 |
0.39 |
k=1.75, b=0.0 |
prefer popular documents, boost multiple keyword hits |