Multimodal recommender system based on multi-channel counterfactual learning networks

Most multimodal recommender systems utilize multimodal content of user-interacted items as supplemental information to capture user preferences based on historical interactions without considering user-uninteracted items. In contrast, multimodal recommender systems based on causal inference counterfactual learning utilize the causal difference between the multimodal content of user-interacted and user-uninteracted items to purify the content related to user preferences. However, existing methods adopt a unified multimodal channel, which treats each modality equally, resulting in the inability to distinguish users’ tastes for different modalities. Therefore, the differences in users’ attention and perception of different modalities' content cannot be reflected. To cope with the above issue, this paper proposes a novel recommender system based on multi-channel counterfactual learning (MCCL) networks to capture user fine-grained preferences on different modalities. First, two independent channels are established based on the corresponding features for the content of image and text modalities for modality-specific feature extraction. Then, leveraging the counterfactual theory of causal inference, features in each channel unrelated to user preferences are eliminated using the features of the user-uninteracted items. Features related to user preferences are enhanced and multimodal user preferences are modeled at the content level, which portrays the users' taste for the different modalities of items. Finally, semantic entities are extracted to model semantic-level multimodal user preferences, which are fused with historical user interaction information and content-level user preferences for recommendation. Extensive experiments on three different datasets show that our results improve up to 4.17% on NDCG compared to the optimal model.