Awesome

ELMRec (EMNLP'24)

Paper - [ArXiv] [ACL Anthology]

Enhancing High-order Interaction Awareness in LLM-based Recommender Model, EMNLP 2024 Main.
Xinfeng Wang, Jin Cui, Fumiyo Fukumoto, and Yoshimi Suzuki.

LOGO

Train and test ELMRec

(1) Install requirements

    >> pip install -r requirements.txt

(2) Pretraining ({dataset}: beauty, sports, and toys.) (under ./ELMRec )

    >> python pretrain.py ./data/{dataset}/ --cuda --batch_size 64 --checkpoint ./checkpoint/{dataset}/

(3) Inference ({dataset}: beauty, sports, and toys.) (under ./ELMRec )

    >> python seq_reranker.py ./data/{dataset}/ --cuda --batch_size 16 --checkpoint ./checkpoint/{dataset}/
    >> python topn.py ./data/{dataset}/ --cuda --batch_size 16 --checkpoint ./checkpoint/{dataset}/
    >> python exp.py ./data/{dataset}/ --cuda --batch_size 16 --checkpoint ./checkpoint/{dataset}/

Note

The checkpoints of ELMRec were uploaded on Google Drive.
If you have any questions, please feel free to contact me at kaysenn@163.com.
Thanks to linglingl635's careful check, I could recheck and correct Table 9. If you have any questions about this, please check the latest version on arXiv.

Parameters (Table 9)

<table> <tr> <th>Dataset</th> <th colspan="4">Direct Recommendation</th> <th colspan="4">Sequential Recommendation</th> </tr> <tr> <td></td> <td>α</td> <td>σ</td> <td>N</td> <td>L</td> <td>α</td> <td>σ</td> <td>N</td> <td>L</td> </tr> <tr> <td>Sports</td> <td>5</td> <td>5</td> <td>10</td> <td>4</td> <td>1</td> <td>5</td> <td>10</td> <td>4</td> </tr> <tr> <td>Beauty</td> <td>9</td> <td>6</td> <td>15</td> <td>4</td> <td>6</td> <td>6</td> <td>15</td> <td>4</td> </tr> <tr> <td>Toys</td> <td>11</td> <td>5</td> <td>10</td> <td>4</td> <td>9</td> <td>5</td> <td>10</td> <td>4</td> </tr> </table>

Code Reference

Citation

If this repository helps you, please cite:

@inproceedings{wang2024enhancing,
  title={Enhancing High-order Interaction Awareness in LLM-based Recommender Model},
  author={Wang, Xinfeng and Cui, Jin and Fukumoto, Fumiyo and Suzuki, Yoshimi},
  booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  pages={11696--11711},
  year={2024}
}