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| ||np1042 ||0.70 ||0.50 ||0.50 ||b=0.2, k=0.8 ||None (baseline) || | ||np1042 ||0.73 ||0.54 ||0.55 ||b=0.2, k=0.8 ||None (baseline) || ||mr488 ||0.72 ||0.57 ||0.56 ||b=0.1, k=1 || log(imdb) || ||nw164 ||0.76 ||0.56 ||0.55 ||b=0.1, k=1.05 || Penalty for short and common words, removed words from query, bonus for higher ranked movies|| ||mb791 ||0.61 ||0.56 ||0.55 ||b=0.12, k=0.9 || log(# imdb votes), removed words from queries || ||do67 ||0.79 ||0.54 ||0.55 ||b=0.2, k=0.8 || multiply with number votes mapped to a number between 1 and 2 || ||as1936||0.79 ||0.57 ||0.58 ||b=0.15, k=0.85 || Filtering commonly used words, bonus if the words in query are part of the title, adding the imdb score, adding the log(number_of_votes)|| ||mn279 ||0.81 ||0.54 ||0.55 ||b=0.1, k=0.75 || bonus if the words in query are part of the title, removed words from queries || ||jm700 ||0.79 ||0.56 ||0.56 ||b=0.08, k=0.9 || ignore words with 2 characters or less || ||gf52 ||0.75 ||0.54 ||0.56 ||b=0.15, k=1.35 || added blacklist of common english words, included parameters for weighting the impact of IMDb votes, ratings and Wikimedia pages || ||ek223||0.76 ||0.55 ||0.56 || b=0.08, k=0.70|| ignore short words, reduce influence of common words|| ||cw441||0.85 ||0.54 ||0.56 || b=0, k=0.5|| ignore short words, bonus for higher ranked movies|| ||ls1369||0.70 ||0.56 ||0.53 || b=0.1, k=0.9||remove single word delimeter words from queries and add the log of the iMDB votes to the score|| ||ap367||0.79 ||0.54 ||0.54 || b=0.04, k=1.15 || None || ||cd100||0.76 ||0.51 ||0.54 || b=0.09, k=0.81 || 10% score boost for docs containing all words (which unfortunately seems to be bad for the metrics) || ||bo30||0.84 ||0.52 ||0.52 || b=0.02, k=0.59 || ignore short words, random search for b and k values || ||bs249||0.79||0.55||0.53|| b=0, k=1.1 || None || |
Results for Exercise Sheet 2 (Ranking)
These results should be based on the file movies.test-benchmark.tsv. Click "Edit" in the upper bar of this page and 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 account name.
Name |
MP@3 |
MP@R |
MAP |
BM25 Parameters |
Refinements |
np1042 |
0.73 |
0.54 |
0.55 |
b=0.2, k=0.8 |
None (baseline) |
mr488 |
0.72 |
0.57 |
0.56 |
b=0.1, k=1 |
log(imdb) |
nw164 |
0.76 |
0.56 |
0.55 |
b=0.1, k=1.05 |
Penalty for short and common words, removed words from query, bonus for higher ranked movies |
mb791 |
0.61 |
0.56 |
0.55 |
b=0.12, k=0.9 |
log(# imdb votes), removed words from queries |
do67 |
0.79 |
0.54 |
0.55 |
b=0.2, k=0.8 |
multiply with number votes mapped to a number between 1 and 2 |
as1936 |
0.79 |
0.57 |
0.58 |
b=0.15, k=0.85 |
Filtering commonly used words, bonus if the words in query are part of the title, adding the imdb score, adding the log(number_of_votes) |
mn279 |
0.81 |
0.54 |
0.55 |
b=0.1, k=0.75 |
bonus if the words in query are part of the title, removed words from queries |
jm700 |
0.79 |
0.56 |
0.56 |
b=0.08, k=0.9 |
ignore words with 2 characters or less |
gf52 |
0.75 |
0.54 |
0.56 |
b=0.15, k=1.35 |
added blacklist of common english words, included parameters for weighting the impact of IMDb votes, ratings and Wikimedia pages |
ek223 |
0.76 |
0.55 |
0.56 |
b=0.08, k=0.70 |
ignore short words, reduce influence of common words |
cw441 |
0.85 |
0.54 |
0.56 |
b=0, k=0.5 |
ignore short words, bonus for higher ranked movies |
ls1369 |
0.70 |
0.56 |
0.53 |
b=0.1, k=0.9 |
remove single word delimeter words from queries and add the log of the iMDB votes to the score |
ap367 |
0.79 |
0.54 |
0.54 |
b=0.04, k=1.15 |
None |
cd100 |
0.76 |
0.51 |
0.54 |
b=0.09, k=0.81 |
10% score boost for docs containing all words (which unfortunately seems to be bad for the metrics) |
bo30 |
0.84 |
0.52 |
0.52 |
b=0.02, k=0.59 |
ignore short words, random search for b and k values |
bs249 |
0.79 |
0.55 |
0.53 |
b=0, k=1.1 |
None |