Package | Description |
---|---|
net.librec.recommender.baseline | |
net.librec.recommender.cf | |
net.librec.recommender.cf.ranking | |
net.librec.recommender.cf.rating |
Modifier and Type | Class and Description |
---|---|
class |
ItemClusterRecommender
It is a graphical model that clusters items into K groups for recommendation, as opposite to
the
UserCluster recommender. |
class |
UserClusterRecommender
It is a graphical model that clusters users into K groups for recommendation, see reference: Barbieri et al.,
Probabilistic Approaches to Recommendations (Section 2.2), Synthesis Lectures on Data Mining and
Knowledge Discovery, 2014.
|
Modifier and Type | Class and Description |
---|---|
class |
BHFreeRecommender
Barbieri et al., Balancing Prediction and Recommendation Accuracy: Hierarchical Latent Factors for Preference
Data, SDM 2012.
|
class |
BUCMRecommender
Bayesian UCM: Nicola Barbieri et al., Modeling Item Selection and Relevance for Accurate Recommendations: a
Bayesian Approach, RecSys 2011.
|
Modifier and Type | Class and Description |
---|---|
class |
AspectModelRecommender
Latent class models for collaborative filtering
|
class |
ItemBigramRecommender
Hanna M.
|
class |
LDARecommender
Latent Dirichlet Allocation for implicit feedback: Tom Griffiths, Gibbs sampling in the generative model of
Latent Dirichlet Allocation, 2002.
|
class |
PLSARecommender
Thomas Hofmann, Latent semantic models for collaborative filtering,
ACM Transactions on Information Systems.
|
Modifier and Type | Class and Description |
---|---|
class |
GPLSARecommender
Thomas Hofmann, Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis, SIGIR
2003.
|
class |
LDCCRecommender |
class |
URPRecommender
User Rating Profile: a LDA model for rating prediction.
|
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