Pengfei Zhang1, Rui Xin1, Xing Xu1, Yuzhen Wang1, Xiaodong Li2, Xiao Zhang2, Meina Song2, Zhonghong Ou3,*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5413-5428, 2025, DOI:10.32604/cmc.2025.062618
- 19 May 2025
Abstract Session-based recommendation systems (SBR) are pivotal in suggesting items by analyzing anonymized sequences of user interactions. Traditional methods, while competent, often fall short in two critical areas: they fail to address potential inter-session item transitions, which are behavioral dependencies that extend beyond individual session boundaries, and they rely on monolithic item aggregation to construct session representations. This approach does not capture the multi-scale and heterogeneous nature of user intent, leading to a decrease in modeling accuracy. To overcome these limitations, a novel approach called HMGS has been introduced. This system incorporates dual graph architectures to… More >
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