Index of the consensus module
-
m
consensus
...
- Models for prediction of user ratings of items using collaborative filtering.
-
C
DistanceModel
...
- Uses the vector distance in n-dimensional space defined by the users' ratings for n items
-
f
getNeighbors
...
- Get a sorted list of neighbors for the given judge, where neighbors
are defined as judges with preferences most similar to the given judge.
-
f
getRecommendations
...
- Return a list of items and their scores (how relevant they are to
the given judge), sorted from most recommended to least recommended.
-
f
__init__
...
- Initialize the model with a group of judges and their rating histories
-
f
similarity
...
- See Model.similarity for argument information.
-
C
SimpleSimilarityModel
...
- Uses a simple function where a larger intersection of preferred items results in greater similarity
-
f
getNeighbors
...
- Get a sorted list of neighbors for the given judge, where neighbors
are defined as judges with preferences most similar to the given judge.
-
f
getRecommendations
...
- Return a list of items and their scores (how relevant they are to
the given judge), sorted from most recommended to least recommended.
-
f
__init__
...
- Initialize the model with a group of judges and their rating histories
-
f
similarity
...
- See Model.similarity for argument information.
-
C
SMSimilarityModel
...
- Uses the constrained Pearson correlation function (Shardanand & Maes 1995)
-
f
getNeighbors
...
- Get a sorted list of neighbors for the given judge, where neighbors
are defined as judges with preferences most similar to the given judge.
-
f
getRecommendations
...
- Return a list of items and their scores (how relevant they are to
the given judge), sorted from most recommended to least recommended.
-
f
__init__
...
- Initialize the model with a group of judges and their rating histories
-
f
similarity
...
- See Model.similarity for argument information.
-
C
Model
...
- The base class for all collaborative filtering models.
Not usable unles subclassed.
-
f
getNeighbors
...
- Get a sorted list of neighbors for the given judge, where neighbors
are defined as judges with preferences most similar to the given judge.
-
f
getRecommendations
...
- Return a list of items and their scores (how relevant they are to
the given judge), sorted from most recommended to least recommended.
-
f
__init__
...
- Initialize the model with a group of judges and their rating histories
-
f
similarity
...
- Calculate the similarity of judges j1 and j2.