This is our tensorflow implementation for the paper:
Shuaiyang Li, Dan Guo, Kang Liu, Richang Hong, and Feng Xue. 2023. Multimodal Counterfactual Learning Network for Multimedia-based Recommendation. In SIGIR. ACM, 1539–1548.
Multimedia-based recommendation (MMRec) utilizes multimodal content (images, textual descriptions, etc.) as auxiliary information on historical interactions to determine user preferences. Most MMRec approaches predict user interests by exploiting a large amount of multimodal contents of user-interacted items, ignoring the potential effect of multimodal content of user-uninteracted items. As a matter of fact, there is a small portion of user preference-irrelevant features in the multimodal content of user-interacted items, which may be a kind of spurious correlation with user preferences, thereby degrading the recommendation performance. In this work, we argue that the multimodal content of user-uninteracted items can be further exploited to identify and eliminate the user preference-irrelevant portion inside user-interacted multimodal content, for example by counterfactual inference of causal theory. Going beyond multimodal user preference modeling only using interacted items, we propose a novel model called Multimodal Counterfactual Learning Network (MCLN), in which user-uninteracted items’ multimodal content is additionally exploited to further purify the representation of user preference-relevant multimodal content that better matches the user’s interests, yielding state-of-the-art performance. Extensive experiments are conducted to validate the effectiveness and rationality of MCLN.
Before running the codes, please download the datasets and copy them to the Data directory.
- Tensorflow 1.10.0
- Python 3.6
- NVIDIA GPU + CUDA + CuDNN
If our paper and codes are useful to you, please cite:
@inproceedings{MCLN,
title={Multimodal Counterfactual Learning Network for Multimedia-based Recommendation},
author={Li, Shuaiyang and Guo, Dan and Liu, Kang and Hong, Richang and Xue, Feng},
booktitle={Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages={1539--1548},
year={2023}
}