• RecStudio
• • Description
  • • Model Structure
    • Dataset Structure
    • Model Evaluation
    • ANNs & Sampler
    • Loss & Score
  • Features
  • Quick Start
  • Automatic Hyper-parameter Tuning
  • Contributing
  • The Team
  • License

RecStudio

RecStudio is a unified, highly-modularized and recommendation-efficient recommendation library based on PyTorch. All the algorithms are
categorized as follows according to recommendation tasks.

• General Recommendation
• Sequential Recommendation
• Knowledge-based Recommendation
• Feature-based Recommendation
• Social Recommendation

Description

Model Structure

At the core of the library, all recommendation models are grouped into three base classes:

• TowerFreeRecommender: The most flexible base class, which enables any complex feature-interaction modeling.
• ItemTowerRecommender: Item encoders are separated from recommender, enabling fast ANN and model-based negative sampling.
• TwoTowerRecommender: The subclass of ItemTowerRecommender, where recommenders only consist of user encoder and item encoder.

Dataset Structure

For the dataset structure, the datasets are divided into five categories:

Dataset	Application	Examples
TripletDataset	Dataset for providing user-item-rating triplet	BPR, NCF, CML et al.
UserDataset	Dataset for AutoEncoder-based ItemTowerRecommender	MultiVAE, RecVAE, et al.
SeqDataset	Dataset for Sequential recommenders with Causal Prediction	GRU4Rec, SASRec, et al.
Seq2SeqDataset	Dataset for Sequential recommenders with Masked Prediction	Bert4Rec, et al.
ALSDataset	Dataset for recommenders optimized by alternating least square	WRMF, et al.

In order to accelerate dataset processing, processed dataset are automatically cached for repeatable training shortly.

Model Evaluation

Almost all common metrics used in recommender systems are implemented in RecStudio based on PyTorch, such as NDCG, Recall, Precision, et al. All metric functions have the same interface, being fully implemented with tensor operators. Therefore, the evaluation procedure can be moved to GPU, leading to a remarkable speedup of evaluation.

ANNs & Sampler

In order to accelerate training and evaluation, RecStudio integrates various Approximate Nearest Neighbor
search (ANNs) and negative samplers. By building indexes with ANNs, the topk operator based on Euclidean distance, inner product and cosine similarity can be significantly accelerated. Negative samplers consist of static sampler and model-based samplers developed by RecStudio
team. Static samplers consist of Uniform Sampler and Popularity Sampler. The model-based samplers are based on either quantization of item vectors or importance resampling. Moreover, we also implement static sampling in the dataset, which enables us to generate negatives when loading data.

Loss & Score

In RecStudio, loss functions are categorized into three types:
- FullScoreLoss: Calculating scores on the whole items, such as SoftmaxLoss.
- PairwiseLoss: Calculating scores on positive and negative items, such as BPRLoss,
BinaryCrossEntropyLoss, et al.
- PointwiseLoss: Calculating scores for a single (user, item) interaction, such as HingeLoss.

Score functions are used to model users' preference on items. Various common score functions are
implemented in RecStudio, such as InnerProduct, EuclideanDistance, CosineDistance, MLPScorer,
et al.

Loss	Math Type	Sampling Distribution	Calculation Complexity	Sampling Complexity	Convergence Speed	Related Metrics
Softmax		No sampling		-	very fast	NDCG
Sampled Softmax		No sampling		-	fast	NDCG
BPR		Uniform sampling			slow	AUC
WARP		Reject Sampling		slower and slower	slow	Precision
InfoNCE		Popularity sampling			fast	DCG
WRMF		No sampling		-	very fast	-
PRIS		Cluster sampling			very fast	DCG

Figure: RecStudio Framework

Features

• General Dataset Structure A unified dataset config based on atomic data files and automatic data cache
  are supported in RecStudio.
• Modular Model Structure By organizing the whole recommender into different modules, loss functions, scoring
  functions, samplers and ANNs, you can customize your model like building blocks.
• GPU Acceleration The whole operation from model training to model evaluation could be easily moved to
  on GPUs and distributed GPUs for running.
• Simple Model Categorization RecStudio categorizes all the models based on the number of encoders,
  which is easy to understand and use. The taxonomy can cover all models.
• Simple and Complex Negative Samplers RecStudio integrates static and model-based samplers with only tensor operators.

Quick Start

By downloading the source code, you can run the provided script run.py for initial usage of RecStudio.

python run.py

The initial config will train and evaluate BPR model on MovieLens-100k(ml-100k) dataset.

Generally speaking, the simple example will take less than one minute with GPUs. And the output will be
like below:

[2023-08-24 10:51:41] INFO Log saved in /home/RecStudio/log/BPR/ml-100k/2023-08-24-10-51-41-738329.log.
[2023-08-24 10:51:41] INFO Global seed set to 2022
[2023-08-24 10:51:41] INFO dataset is read from /home/RecStudio/recstudio/dataset_demo/ml-100k.
[2023-08-24 10:51:42] INFO 
Dataset Info: 

=============================================================================
interaction information: 
field      user_id    item_id    rating     timestamp  
type       token      token      float      float      
##         944        1575       -          -          
=============================================================================
user information: 
field      user_id    age        gender     occupation zip_code   
type       token      token      token      token      token      
##         944        62         3          22         795        
=============================================================================
item information: 
field      item_id    
type       token      
##         1575       
=============================================================================
Total Interactions: 82520
Sparsity: 0.944404
=============================================================================
timestamp=StandardScaler()
[2023-08-24 10:51:42] INFO 
Model Config: 

data:
	binarized_rating_thres=None
	fm_eval=False
	neg_count=0
	sampler=None
	shuffle=True
	split_mode=user_entry
	split_ratio=[0.8, 0.1, 0.1]
	fmeval=False
	binaried_rating_thres=0.0
eval:
	batch_size=20
	cutoff=[5, 10, 20]
	val_metrics=['ndcg', 'recall']
	val_n_epoch=1
	test_metrics=['ndcg', 'recall', 'precision', 'map', 'mrr', 'hit']
	topk=100
	save_path=./saved/
model:
	embed_dim=64
	item_bias=False
train:
	accelerator=gpu
	ann=None
	batch_size=512
	early_stop_mode=max
	early_stop_patience=10
	epochs=1000
	gpu=1
	grad_clip_norm=None
	init_method=xavier_normal
	item_batch_size=1024
	learner=adam
	learning_rate=0.001
	num_threads=10
	sampling_method=none
	sampler=uniform
	negative_count=1
	excluding_hist=False
	scheduler=None
	seed=2022
	weight_decay=0.0
	tensorboard_path=None
[2023-08-24 10:51:42] INFO Tensorboard log saved in ./tensorboard/BPR/ml-100k/2023-08-24-10-51-41-738329.
[2023-08-24 10:51:42] INFO The default fields to be used is set as [user_id, item_id, rating]. If more fields are needed, please use `self._set_data_field()` to reset.
[2023-08-24 10:51:42] INFO save_dir:./saved/
[2023-08-24 10:51:42] INFO BPR(
  (score_func): InnerProductScorer()
  (loss_fn): BPRLoss()
  (item_encoder): Embedding(1575, 64, padding_idx=0)
  (query_encoder): Embedding(944, 64, padding_idx=0)
  (sampler): UniformSampler()
)
[2023-08-24 10:51:42] INFO GPU id [8] are selected.
[2023-08-24 10:51:45] INFO Training: Epoch=  0 [ndcg@5=0.0111 recall@5=0.0044 train_loss_0=0.6931]
[2023-08-24 10:51:45] INFO Train time: 0.88524s. Valid time: 0.18036s. GPU RAM: 0.03/10.76 GB
[2023-08-24 10:51:45] INFO ndcg@5 improved. Best value: 0.0111
[2023-08-24 10:51:45] INFO Best model checkpoint saved in ./saved/BPR/ml-100k/2023-08-24-10-51-41-738329.ckpt.
...
[2023-08-24 10:52:08] INFO Training: Epoch= 34 [ndcg@5=0.1802 recall@5=0.1260 train_loss_0=0.1901]
[2023-08-24 10:52:08] INFO Train time: 0.41784s. Valid time: 0.32394s. GPU RAM: 0.03/10.76 GB
[2023-08-24 10:52:08] INFO Early stopped. Since the metric ndcg@5 haven't been improved for 10 epochs.
[2023-08-24 10:52:08] INFO The best score of ndcg@5 is 0.1807 on epoch 24
[2023-08-24 10:52:08] INFO Best model checkpoint saved in ./saved/BPR/ml-100k/2023-08-24-10-51-41-738329.ckpt.
[2023-08-24 10:52:08] INFO Testing:  [ndcg@5=0.2389 recall@5=0.1550 precision@5=0.1885 map@5=0.1629 mrr@5=0.3845 hit@5=0.5705 ndcg@10=0.2442 recall@10=0.2391 precision@10=0.1498 map@10=0.1447 mrr@10=0.4021 hit@10=0.6999 ndcg@20=0.2701 recall@20=0.3530 precision@20=0.1170 map@20=0.1429 mrr@20=0.4109 hit@20=0.8240]

If you want to change models or datasets, command line is ready for you.

python run.py -m=NCF -d=ml-1m

• Supported command line arguments:

  args	type	description	default	optional
  -m,--model	str	model name	BPR	all the models in RecStudio
  -d,--dataset	str	dataset name	ml-100k	all the datasets supported by RecStudio
  --data_dir	str	dataset folder	datasets	folders that could be read by RecStudio
  mode	str	training mode	light	['light','detail','tune']
  --learning_rate	float	learning rate	0.001
  --learner	str	optimizer name	adam	['adam','sgd','adasgd','rmsprop','sparse_adam']
  --weight_decay	float	weight decay for optimizer	0
  --epochs	int	training epoch	20,50
  --batch_size	int	the size of mini batch in training	2048
  --eval_batch_size	int	the size of mini batch in evaluation	128
  --embed_dim	int	the output size of embedding layers	64

• For ItemTowerRecommender, some extra args are supported:

  args	type	description	default	optional
  --sampler	str	sampler name	uniform	['uniform','popularity','midx_uni','midx_pop','cluster_uni','cluster_pop']
  --negative_count	int	number of negative samples	1	positive integer

• For TwoTowerRecommender, some extra args are supported based on ItemTowerRecommender:

  args	type	description	default	optional
  --split_mode	str	split methods for the dataset	user_entry	['user','entry','user_entry']

Here are some details of some unclear arguments.

1. mode: in light mode and detail mode, the output will displayed on the terminal, while
   the latter provide more detailed info. tune mode will use Neural Network Intelligence(NNI) to show
   a beautiful visual interface. You can run tune.sh with a config file like config.yaml. For
   more details about NNI, please refer to NNI Documentation.
2. sampler: uniform stands for UniformSampler is used. popularity stands for sampling according
   to the item popularity (more popular items are sampled with higher probabilities). midx_uni,midx_pop
   are midx dynamic sampler, please refer to FastVAE for more details.
   cluster_uni,cluster_pop are cluster dynamic sampler, please refer to
   PRIS for more details.
3. split_mode: user means splitting all users into train/valid/test datasets, users in
   those datasets are disjoint. entry means spliting all the interactions in those three dataset.
   user_entry means spliting interaction of each user into three parts.

Also, you can install RecStudio from PyPi:

pip install recstudio

For basic usage like below:

import recstudio
recstudio.run(model="BPR", data_dir="./datasets/", dataset='ml-100k')

For more detailed information, please refer to our documentation http://recstudio.org.cn/docs/.

Automatic Hyper-parameter Tuning

RecStudio integrates with NNI module for tuning the hype-parameters automatically. For easy usage,
you can run the following command in bash:

nnictl create --config ./nni-experiments/config/bpr.yaml --port 2023

Configure nni-experiments/config/bpr.yaml and nni-experiments/search_space/bpr.yaml for personal usage.

For more detailed information about NNI, please refer to
NNI Documentation.

Contributing

Please let us know if you encounter a bug or have any suggestions by
submitting an issue.

We welcome all contributions from bug fixes to new features and extensions.

We expect all contributions firstly discussed in the issue tracker and then going through PRs.

The Team

RecStudio is developed and maintained by USTC BigData Lab.

User	Contributions
@DefuLian	Framework design and construction
@AngusHuang17	Sequential model, docs, bugs fixing
@Xiuchen519	Knowledge-based model, bugs fixing
@JennahF	NCF,CML,logisticMF models
@HERECJ	AutoEncoder models
@BinbinJin	IRGAN model
@pepsi2222	Ranking models
@echobelbo	Docs
@jinbaobaojhr	Docs

License

RecStudio uses MIT License.