Modifier and Type | Class and Description |
---|---|
class |
FactorizationMachineRecommender
Factorization Machine Recommender
Rendle, Steffen, et al., Fast Context-aware Recommendations with Factorization Machines, SIGIR, 2011.
|
class |
MatrixFactorizationRecommender
Matrix Factorization Recommender
Methods with user factors and item factors: such as SVD(Singular Value Decomposition)
|
class |
ProbabilisticGraphicalRecommender
Created by Keqiang Wang
|
class |
SocialRecommender
Social Recommender
|
Modifier and Type | Class and Description |
---|---|
class |
ConstantGuessRecommender
Baseline: predict by a constant rating
|
class |
GlobalAverageRecommender
Baseline: predict by average rating of all users
|
class |
ItemAverageRecommender
Baseline: predict by the average of target item's ratings
|
class |
ItemClusterRecommender
It is a graphical model that clusters items into K groups for recommendation, as opposite to
the
UserCluster recommender. |
class |
MostPopularRecommender
Baseline: items are weighted by the number of ratings they received.
|
class |
RandomGuessRecommender
Baseline: predict by a random value in (minRate, maxRate)
|
class |
UserAverageRecommender
Baseline: predict by the average of target user's ratings
|
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.
|
class |
ItemKNNRecommender
ItemKNNRecommender
|
class |
UserKNNRecommender
UserKNNRecommender
|
Modifier and Type | Class and Description |
---|---|
class |
AoBPRRecommender
AoBPR: BPR with Adaptive Oversampling
|
class |
AspectModelRecommender
Latent class models for collaborative filtering
|
class |
BPRRecommender
Rendle et al., BPR: Bayesian Personalized Ranking from Implicit Feedback, UAI 2009.
|
class |
CLIMFRecommender
Shi et al., Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering.,
RecSys 2012.
|
class |
EALSRecommender
EALS: efficient Alternating Least Square for Weighted Regularized Matrix Factorization.
|
class |
FISMaucRecommender
Kabbur et al., FISM: Factored Item Similarity Models for Top-N Recommender Systems, KDD 2013.
|
class |
FISMrmseRecommender
Kabbur et al., FISM: Factored Item Similarity Models for Top-N Recommender Systems, KDD 2013.
|
class |
GBPRRecommender
Pan and Chen, GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative
Filtering, IJCAI 2013.
|
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 |
ListRankMFRecommender
Shi et al., List-wise learning to rank with matrix factorization for
collaborative filtering, RecSys 2010.
|
class |
PLSARecommender
Thomas Hofmann, Latent semantic models for collaborative filtering,
ACM Transactions on Information Systems.
|
class |
RankALSRecommender
Takacs and Tikk,
Alternating Least Squares for Personalized Ranking
, RecSys 2012.
|
class |
RankSGDRecommender
Jahrer and Toscher, Collaborative Filtering Ensemble for Ranking, JMLR, 2012 (KDD Cup 2011 Track 2).
|
class |
SLIMRecommender
Xia Ning and George Karypis, SLIM: Sparse Linear Methods for Top-N Recommender Systems, ICDM 2011.
|
class |
WBPRRecommender
Gantner et al., Bayesian Personalized Ranking for Non-Uniformly Sampled Items, JMLR, 2012.
|
class |
WRMFRecommender
WRMF: Weighted Regularized Matrix Factorization.
|
Modifier and Type | Class and Description |
---|---|
class |
ASVDPlusPlusRecommender
Yehuda Koren, Factorization Meets the Neighborhood: a Multifaceted
Collaborative Filtering Model., KDD 2008.
|
class |
BiasedMFRecommender
Biased Matrix Factorization Recommender
|
class |
BPMFRecommender
Salakhutdinov and Mnih, Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo,
ICML 2008.
|
class |
BPoissMFRecommender
Prem Gopalan, et al.
|
class |
FMALSRecommender
Factorization Machine Recommender via Alternating Least Square
|
class |
FMSGDRecommender
Stochastic Gradient Descent with Square Loss
Rendle, Steffen, "Factorization Machines", Proceedings of the 10th IEEE International Conference on Data Mining, 2010
Rendle, Steffen, "Factorization Machines with libFM", ACM Transactions on Intelligent Systems and Technology, 2012
|
class |
GPLSARecommender
Thomas Hofmann, Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis, SIGIR
2003.
|
class |
LDCCRecommender |
class |
LLORMARecommender
Local Low-Rank Matrix Approximation
|
class |
MFALSRecommender
The class implementing the Alternating Least Squares algorithm
|
class |
NMFRecommender
Daniel D.
|
class |
PMFRecommender
PMF: Ruslan Salakhutdinov and Andriy Mnih, Probabilistic Matrix Factorization, NIPS 2008.
RegSVD: Arkadiusz Paterek, Improving Regularized Singular Value Decomposition
Collaborative Filtering, Proceedings of KDD Cup and Workshop, 2007.
|
class |
RBMRecommender
This class implementing user-oriented Restricted Boltzmann Machines for
Collaborative Filtering
|
class |
RFRecRecommender
Gedikli et al., RF-Rec: Fast and Accurate Computation of
Recommendations based on Rating Frequencies, IEEE (CEC) 2011,
Luxembourg, 2011, pp.
|
class |
SVDPlusPlusRecommender
SVD++ Recommender
|
class |
URPRecommender
User Rating Profile: a LDA model for rating prediction.
|
Modifier and Type | Class and Description |
---|---|
class |
SBPRRecommender
Social Bayesian Personalized Ranking (SBPR)
|
Modifier and Type | Class and Description |
---|---|
class |
RSTERecommender
Hao Ma, Irwin King and Michael R.
|
class |
SocialMFRecommender
Jamali and Ester, A matrix factorization technique with trust propagation for recommendation in social
networks, RecSys 2010.
|
class |
SoRecRecommender
Jamali and Ester, A matrix factorization technique with trust propagation for recommendation in social
networks, RecSys 2010.
|
class |
SoRegRecommender
Hao Ma, Dengyong Zhou, Chao Liu, Michael R.
|
class |
TimeSVDRecommender
TimeSVD++ Recommender
|
class |
TrustMFRecommender
Yang et al., Social Collaborative Filtering by Trust, IJCAI 2013.
|
class |
TrustSVDRecommender
Guo et al., TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and
of Item Ratings, AAAI 2015.
|
Modifier and Type | Class and Description |
---|---|
class |
AssociationRuleRecommender
Choonho Kim and Juntae Kim, A Recommendation Algorithm Using Multi-Level Association Rules, WI 2003.
|
class |
ExternalRecommender
Suppose that you have some predictive ratings (in "pred.txt") generated by an external recommender (e.g., some
recommender of MyMediaLite).
|
class |
PersonalityDiagnosisRecommender
Related Work:
A brief introduction to Personality
Diagnosis
|
class |
PRankDRecommender
Neil Hurley, Personalised ranking with diversity, RecSys 2013.
|
class |
SlopeOneRecommender
Weighted Slope One: Lemire and Maclachlan,
Slope One Predictors for Online Rating-Based Collaborative Filtering
, SDM 2005.
|
Modifier and Type | Class and Description |
---|---|
class |
HybridRecommender
Zhou et al., Solving the apparent diversity-accuracy dilemma of recommender systems, Proceedings of
the National Academy of Sciences, 2010.
|
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