In real recommendation scenarios, users often have different types of
behaviors, such as clicking and buying. Existing research methods show that it
is possible to capture the heterogeneous interests of users through different
types of behaviors. However, most multi-behavior approaches have limitations in
learning the relationship between different behaviors. In this paper, we
propose a novel multilayer perceptron (MLP)-based heterogeneous sequential
recommendation method, namely behavior-aware multilayer perceptron (BMLP).
Specifically, it has two main modules, including a heterogeneous interest
perception (HIP) module, which models behaviors at multiple granularities
through behavior types and transition relationships, and a purchase intent
perception (PIP) module, which adaptively fuses subsequences of auxiliary
behaviors to capture users' purchase intent. Compared with mainstream sequence
models, MLP is competitive in terms of accuracy and has unique advantages in
simplicity and efficiency. Extensive experiments show that BMLP achieves
significant improvement over state-of-the-art algorithms on four public
datasets. In addition, its pure MLP architecture leads to a linear time
complexity.

基于多层感知器 (MLP) 的异构顺序推荐方法 (BMLP) 通过建模多种行为类型和转换关系的异构兴趣知觉模块 (HIP) 以及自适应融合辅助行为子序列以捕捉用户购买意图的购买意图知觉模块 (PIP)，相比主要的序列模型，在准确性方面具有竞争优势并具有简单和高效的独特优势，广泛实验表明 BMLP 在四个公共数据集上显著改进了现有算法，而其纯 MLP 架构导致线性时间复杂度。