Model Introduction ===================== We implement 91 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. .. toctree:: :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 model/general/random model/general/diffrec model/general/ldiffrec 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 model/context/kd_dagfm model/context/fignn model/context/eulernet 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 model/sequential/fearec 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