用于候选项目匹配的可配置、可调和可重现的库_Python_下载.zip

# MatchBox Industrial recommender systems generally have two main stages: matching and ranking. In the first stage, candidate item matching (also known as candidate retrieval) aims for efficient and high-recall retrieval from a large item corpus. MatchBox provides an open source library for candidate item matching, with stunning features in configurability, tunability, and reproducibility. ## Model Zoo | Publication | Model | Paper | Benchmark | |:-----------:|:--------------:|:----------------------------------------------------------------- |:-------------:| | UAI'09 | [MF-BPR](./model_zoo/MF) | [BPR: Bayesian Personalized Ranking from Implicit Feedback](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | [:arrow_upper_right:](./model_zoo/MF/config) | | RecSys'16 | [YoutubeNet](./model_zoo/YoutubeNet) | [Deep Neural Networks for YouTube Recommendations](https://dl.acm.org/doi/10.1145/2959100.2959190) | [:arrow_upper_right:](./model_zoo/YouTubeNet/config) | | CIKM'21 | [MF-CCL](./model_zoo/MF)/ [SimpleX](./model_zoo/SimpleX) | [SimpleX: A Simple and Strong Baseline for Collaborative Filtering](https://arxiv.org/abs/2109.12613) | [:arrow_upper_right:](./model_zoo/SimpleX/config) | ## Dependency We suggest to use the following environment where we test MatchBox only. + CUDA 10.0 + python 3.6 + pytorch 1.0 + PyYAML + pandas + scikit-learn + numpy + h5py + tqdm ## Get Started The code workflow is structured as follows: ```python # Set the dataset config and model config feature_cols = [{...}] # define feature columns label_col = {...} # define label column params = {...} # set data params and model params # Set the feature encoding specs feature_encoder = FeatureEncoder(feature_cols, label_col, ...) # define the feature encoder datasets.build_dataset(feature_encoder, ...) # fit feature_encoder and build dataset # Load data generators train_gen, valid_gen, test_gen = datasets.h5_generator(feature_encoder, ...) # Define a model model = SimpleX(...) # Train the model model.fit(train_gen, valid_gen, ...) # Evaluation model.evaluate(test_gen) ``` #### Run the code For reproducing the experiment results, you can run the benchmarking scripts with the corresponding configs as follows. + --config: The config directory where dataset config and model config are located. + --expid: The experiment id defined in a model config file to set a group of hyper-parameters. + --gpu: The gpu index used for experiment, and -1 for CPU. ```bash cd data/Yelp18/yelp18_m1 python matchbox_convert_data.py cd model_zoo/SimpleX python run_expid.py --config ./config/SimpleX_yelp18_m1 --expid SimpleX_yelp18_m1 --gpu 0 ... python run_expid.py --config ./config/SimpleX_amazonbooks_m1 --expid SimpleX_amazonbooks_m1 --gpu 0 python run_expid.py --config ./config/SimpleX_gowalla_m1 --expid SimpleX_gowalla_m1 --gpu 0 ``` The running logs are also available in each config directory.