Self-attention models have achieved state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user-item interactions. However, they rely on positional embeddings to retain the sequential information, which may break the semantics of item embeddings. In addition, most existing works assume that such sequential dependencies exist solely in the item embeddings, but neglect their existence among the item features. In this work, we propose a novel sequential recommender system (MLP4Rec) based on the recent advances of MLP-based architectures, which is naturally sensitive to the order of items in a sequence. To be specific, we develop a tri-directional fusion scheme to coherently capture sequential, cross-channel and cross-feature correlations. Extensive experiments demonstrate the effectiveness of MLP4Rec over various representative baselines upon two benchmark datasets. The simple architecture of MLP4Rec also leads to the linear computational complexity as well as much fewer model parameters than existing self-attention methods.
翻译:自留模型通过捕捉用户-项目互动之间的相继依赖性,在相继建议系统中实现了最先进的性能,但是,它们依靠定位嵌入来保留顺序信息,这可能会打破项目嵌入的语义;此外,大多数现有工程假设,这种相继依赖性只存在于项目嵌入中,但却忽视了项目特征中的存在。在这项工作中,我们根据基于 MLP 的建筑最近的进展,提出了一个新型的顺序建议系统(MLP4Rec),它自然地对项目顺序顺序的顺序敏感。具体地说,我们制定三向融合计划,以连贯地捕捉顺序、交叉通道和跨地物的关联性。广泛的实验表明,MLP4Rec 相对于两个基准数据集中具有代表性的基线的有效性。 MLP4Rec 简单结构还导致线性计算复杂性,比现有自留方法少得多的模型参数。