BMLP: behavior-aware MLP for heterogeneous sequential recommendation
Frontiers of Computer Science(2024)
摘要
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.
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