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Repo for CIKM 2023 paper: A Two-tier Shared Embedding Method for Review-based Recommender Systems
Reviews are valuable resources that have been widely researched and used to improve the quality of recommendation services. Recent methods use multiple full embedding layers to model various levels of individual preferences,increasing the risk of the data sparsity issue. Although it is a potential way to deal with this issue that models homophily among users who have similar behaviors, the existing approaches are implemented in a coarse-grained way. They calculate user similarities by considering the homophily in their global behaviors but ignore their local behaviors under a specific context. In this paper, we propose a two-tier shared embedding model (TSE), which fuses coarse- and fine-grained ways of modeling homophily. It considers global behaviors to model homophily in a coarse-grained way, and the high-level feature in the process of each user-item interaction to model homophily in a fine-grained way. TSE designs a whole-to-part principle-based process to fuse these ways in the review-based recommendation. Experiments on five real-world datasets demonstrate that TSE significantly outperforms state-of-the-art models. It outperforms the best baseline by 20.50% on the root-mean-square error (RMSE) and 23.96% on the mean absolute error (MAE), respectively.
- File Structure
.\amazon_data [training data]
.\sim_res [save the similarity matrixs]
.\similarity [similarity measures]
.\untils [some tools to papre training data]
.\src
- calculate user similarities and item similarities in three levels
sudo python src/get_review_sim.py
sudo python src/get_user_sim.py
[Note!] We use seta to calculate the review sentiment scores.
The package of seta need to used at root user.
- training the model
python src/main.py
If you find this repository helpful, please consider citing our paper:
@inproceedings{liu-ickm-2023-tse,
author = {Zhen Yang and
Junrui Liu and
Tong Li and
Di Wu and
Shiqiu Yang and
Huan Liu},
editor = {Ingo Frommholz and
Frank Hopfgartner and
Mark Lee and
Michael Oakes and
Mounia Lalmas and
Min Zhang and
Rodrygo L. T. Santos},
title = {A Two-tier Shared Embedding Method for Review-based Recommender Systems},
booktitle = {Proceedings of the 32nd {ACM} International Conference on Information
and Knowledge Management, {CIKM} 2023, Birmingham, United Kingdom,
October 21-25, 2023},
pages = {2928--2938},
publisher = {{ACM}},
year = {2023},
url = {https://doi.org/10.1145/3583780.3614770},
doi = {10.1145/3583780.3614770},
timestamp = {Wed, 22 Nov 2023 13:37:55 +0100},
}