Multimodal data can more comprehensively portray changes in user interests, and thus, multimodal sequential recommendation (MSRS) has gained widespread attention in recent years. However, the MSRS faces two key challenges: (1) how to effectively model long-range dependencies in user interaction sequence; and (2) how to efficiently fuse multimodal features. To address these challenges, this article proposes a novel multimodal sequential recommendation architecture based on pure convolutional neural network (CNN), named PCMSRec. PCMSRec contains two key innovations: first, by using the global receptive field of large kernel convolution, it models the long-range dependencies of multimodal user interaction sequence, breaking through the limitation that existing CNN-based methods can only capture local short-distance dependencies; second, by taking advantage of the high flexibility of the CNN architecture, it models the relationships among multimodal features of items through a carefully designed convolutional layer architecture and fusion strategy. Specifically, PCMSRec consists of two blocks: sequence-feature block and modal block. The sequence-feature block models long-range dependencies in user interaction sequence through large kernel convolutional layer and extracts item features by incorporating a bottleneck architecture. The modal block models the complex relationships between multimodal features using multiple convolutional layer. Experimental results on five public datasets show that PCMSRec outperforms existing methods.

Multimodal sequential recommendation extracts multimodal features from user interaction sequences to improve user sequence modeling accuracy and expressiveness. However, this field still faces two major challenges: (1) how to effectively improve multimodal features synergy in user interaction sequences, and (2) how to reduce the number of model parameters while ensuring recommendation performance. To address these challenges, this article proposes a memory-enhanced multimodal sequential recommendation method with multiple distillation (M3DSRec), which includes four key designs: First, a parameter standardization mixture of experts is used to mitigate multimodal feature distribution imbalance and avoid inter-feature interference. Second, a main-modal cooperative block is designed to fuse features of other modalities based on the primary unimodal feature, thereby enhancing the synergy among multimodal features. Third, a memory-enhanced dynamic cluster is designed, which uses a memory bank to enhance the representation of multimodal features in user interaction sequences. Finally, a multiple distillation strategy is used to align the features and logits of the teacher and student networks at multiple levels. Experimental results on five public datasets show that M3DSRec achieves a balance between recommendation performance and the number of model parameters, with both the teacher and student networks outperforming existing mainstream methods.