(WWW '24) UnifiedSSR: A Unified Framework of Sequential Search and Recommendation

This is the Pytorch implementation of UnifiedSSR for joint learning of user behavior history in both search and recommendation scenarios.

Environments

• python=3.8.17
• torch=1.13.1
• numpy=1.24.4
• pandas=2.0.3
• scikit-learn=1.3.0
• matplotlib=3.7.1
• nltk=3.8.1
• joblib=1.3.0

You can create the environment via conda env create -f unifiedssr_env.yaml.

Run the Codes

1. Pretrain: Customize parameters in utils/parser.py, and then run pretrain.py to pretrain the model. The pretrained model will be saved in models/.
2. Finetune: Modify args.trained_model_path in train.py to specify the path to the pretrained model, and then run train.py to finetune the model.
3. Evaluate: Modify args.tasks in predict.py to specify the path to the trained model, and then run predict.py to evaluate the model. Note that evaluation can only be conducted on one task at a time.

We provide a pretrained model and task-specific finetuned models for the Amazon-CL dataset as follows:

1. Pretrained model: models/Amazon_Clothing/pretrain_recommendation_search/model.pth
2. Finetuned model for search: models/Amazon_Clothing/pretrain_recommendation_search/finetune_search/model.pth
3. Finetuned model for recommendation: models/Amazon_Clothing/pretrain_recommendation_search/finetune_recommendation/model.pth

Datasets

• Original Dataset
  • Amazon dataset can be found in here.
  • JDsearch dataset can be found in here.
• Preprocess
  • Use the provided preprocessed Amazon-CL dataset in datasets/Amazon_Clothing.
  • Feel free to contact the author for more details of the data preprocessing.

Notes: The dataset and model files are large. Please download them from Google Drive and place them in the project folder.

Citation

If you find our codes helpful, please kindly cite the following papers:

@article{unifiedssr,
  author       = {Jiayi Xie and
                  Shang Liu and
                  Gao Cong and
                  Zhenzhong Chen},
  title        = {UnifiedSSR: {A} Unified Framework of Sequential Search and Recommendation},
  journal      = {CoRR},
  volume       = {abs/2310.13921},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2310.13921},
  doi          = {10.48550/ARXIV.2310.13921},
  eprinttype    = {arXiv},
  eprint       = {2310.13921}
}