NIKE-ADIDAS / xpert

Code for XPERT algorithm from Personalized Retrieval over Millions of Items

xpert

Code for XPERT algorithm from Personalized Retrieval over Millions of Items

This folder contains the code for XPERT and the Amazon 1M dataset

Download the data and model:

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1. Download and unzip Amazon-1M dataset as part of Amazon-1M.zip.
2. Download and unzip trained model as part of Amazon-1M-model.zip.

Running the inference code of XPERT on Amazon 1M data:

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1. Set the environment variables in setup.sh file. Set CURR_DIR and PYTHONPATH with current_directory. DATA_PATH would be where the data is present (it would be CURR_DIR/data).

2. Run source setup.sh

3. We have provided a trained model in models/model_24.python, which will be used for evaluation.

4. Run python src/evaluate_XPERT.py configs/evaluation.yaml

Data format:

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1. item_features.txt contains the 768-dimensional embeddings of the Amazon product titles which were exracted from a pretrained 6-layered DistilBERT base mode. The format of each row is: <item_id> <item_embedding>

2. final_data_test.txt and final_data_train.txt contains the test and train data respectively in the following format: <user_id> <label_time> label = List of comma separated: <product_id> which are treated as the label label_time = Timestamp of last reviewed product_id among labels history = List of space separated: <product_id>: which are the user history

feat_data_bxml and user_data_test contains binarized files extracted from the files above, and are shared for fast inference.

Cite as:

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@inproceedings{vemuri2023personalized,
  title={Personalized Retrieval over Millions of Items},
  author={Vemuri, Hemanth and Agrawal, Sheshansh and Mittal, Shivam and Saini, Deepak and Soni, Akshay and Sambasivan, Abhinav V and Lu, Wenhao and Wang, Yajun and Parsana, Mehul and Kar, Purushottam and others},
  booktitle={Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={1014--1022},
  year={2023}
}

Currently added features:

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1. Amazon-1M dataset.
2. Inference model and scripts for XPERT.

Features to add:

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1. Add Amazon-10M dataset also.
2. Add text format for both Amazon-1M and Amazon-10M dataset.
3. Add dataset creation scripts.
4. Add base embedding extractions scripts and model.
5. Add global interest creation (clustering) code.
6. Add training scripts for morph operators.