AD Teaching Wiki:

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

AD Teaching Wiki: InformationRetrievalWS2223/ResultsES2 (last edited 2022-11-05 15:14:02 by 10)