Here is the code and datasets to reproduce the experiment result in our paper "Deep Learning for Sequential Recommendation: Algorithms, InfluentialFactors, and Evaluations"
- GRU4Rec is the code to reproduce the result of basic GRU4Rec, sample size, sample alpha, loss function and data augmentation.
- GRU4Rec-with-dwell-time is the code to reproduce the result of Dwell time.
- GRU4Rec-with-knn is the code to reproduce the result of KNN.
- category-and-behavior is the code to reproduce the result of P-GRU, C-GRU and B-GRU, GRU4Rec(behavior) and GRU4Rec(category).
- attention-mechanism is the code to reproduce the result of Attention mechanism.
- user is the code to reproduce the result of user representation(implicit, embedded and recurrent).
- data_preprocess is the preprocessing code for each dataset.
- RSC15 http://www.kaggle.com/chadgostopp/recsys-challenge-2015
- RSC19 http://www.recsyschallenge.com/2019/
- LastFM http://mir.dcs.gla.ac.uk/lastfm/
Please go to https://docs.google.com/spreadsheets/d/1Qs5KKugzheDMFH3YLNoQl50Z3hxRPAvTaEYavGgb5sc/edit?usp=sharing to find more experiment results that we didn't report in the paper.
- GRU4Rec is the original Theano implementation GRU4Rec.
- GRU4Rec-with-dwell-time is modified based on the original Theano implementation GRU4Rec.
- GRU4Rec-with-knn is the original code for paper "When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation" (http://bit.ly/2nfNldD).
- category-and-behavior is modified based on the TensorFlow implementation GRU4Rec.
- attention-mechanism is modified based on the Pytorch implementation NARM.
- user Implicit and Embedded are modified based on the TensorFlow implementation GRU4Rec. Recurrent is the original code for HRNN (https://github.com/mquad/hgru4rec).