Model Introduction
=====================
We implement 83 recommendation models covering general recommendation, sequential recommendation,
context-aware recommendation and knowledge-based recommendation. A brief introduction to these models are as follows:
General Recommendation
--------------------------
In the class of general recommendation, the interaction of users and items(.inter file) is the only data
that can be used by model. Usually, the models are trained on implicit feedback data and evaluated under the
task of top-n recommendation. All the collaborative filter(CF) based models are classified in this class.
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:maxdepth: 1
model/general/pop
model/general/itemknn
model/general/bpr
model/general/neumf
model/general/convncf
model/general/dmf
model/general/fism
model/general/nais
model/general/spectralcf
model/general/gcmc
model/general/ngcf
model/general/lightgcn
model/general/dgcf
model/general/line
model/general/multivae
model/general/multidae
model/general/macridvae
model/general/cdae
model/general/enmf
model/general/nncf
model/general/ract
model/general/recvae
model/general/ease
model/general/slimelastic
model/general/sgl
model/general/admmslim
model/general/nceplrec
model/general/simplex
model/general/ncl
Context-aware Recommendation
-------------------------------
Context-aware recommendation can be seen as an extension of click-through rate prediction. All the model in this
class can be used for CTR prediction. Usually, the dataset is explicit and contains label field. Other feature fields
are also support for these models. And evaluation is always conducted in the way of binary classification.
.. toctree::
:maxdepth: 1
model/context/lr
model/context/fm
model/context/nfm
model/context/deepfm
model/context/xdeepfm
model/context/afm
model/context/ffm
model/context/fwfm
model/context/fnn
model/context/pnn
model/context/dssm
model/context/widedeep
model/context/din
model/context/dien
model/context/dcn
model/context/dcnv2
model/context/autoint
model/context/xgboost
model/context/lightgbm
Sequential Recommendation
---------------------------------
The task of sequential recommendation(next-item recommendation) is the same as general recommendation which sorts a list of items according
to preference. While the history interactions are organized in sequences and the model tends to characterize
the sequential data. The models of session-based recommendation are also included in this class.
.. toctree::
:maxdepth: 1
model/sequential/fpmc
model/sequential/gru4rec
model/sequential/narm
model/sequential/stamp
model/sequential/caser
model/sequential/nextitnet
model/sequential/transrec
model/sequential/sasrec
model/sequential/bert4rec
model/sequential/srgnn
model/sequential/gcsan
model/sequential/gru4recf
model/sequential/sasrecf
model/sequential/fdsa
model/sequential/s3rec
model/sequential/gru4reckg
model/sequential/ksr
model/sequential/fossil
model/sequential/shan
model/sequential/repeatnet
model/sequential/hgn
model/sequential/hrm
model/sequential/npe
model/sequential/lightsans
model/sequential/sine
model/sequential/core
Knowledge-based Recommendation
---------------------------------
Knowledge-based recommendation introduces an external knowledge graph to enhance general or sequential recommendation.
.. toctree::
:maxdepth: 1
model/knowledge/cke
model/knowledge/cfkg
model/knowledge/ktup
model/knowledge/kgat
model/knowledge/kgin
model/knowledge/ripplenet
model/knowledge/mcclk
model/knowledge/mkr
model/knowledge/kgcn
model/knowledge/kgnnls