Modality Matches Modality: Pretraining Modality-Disentangled Item Representations for Recommendation
Tengyue Han, Pengfei Wang, Shaozhang Niu, Chenliang Li · Proceedings of the ACM Web Conference 2022 · 2022
Recent works have shown the effectiveness of incorporating textual and visual information to tackle the sparsity problem in recommendation scenarios. To fuse these useful heterogeneous modality information, an essential prerequisite is to align these information for modality-robust features learning and semantic understanding. Unfortunately, existing works mainly focus on tackling the learning of common knowledge across modalities, while the specific characteristics of each modality is discarded, which may inevitably degrade the recommendation performance.