Dawei Qiu1, Peng Wu1,*, Xiaoming Zhang2,*, Renjie Xu3
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1753-1769, 2025, DOI:10.32604/cmc.2025.062599
- 16 April 2025
Abstract Recently, many Sequential Recommendation methods adopt self-attention mechanisms to model user preferences. However, these methods tend to focus more on low-frequency information while neglecting high-frequency information, which makes them ineffective in balancing users’ long- and short-term preferences. At the same time, many methods overlook the potential of frequency domain methods, ignoring their efficiency in processing frequency information. To overcome this limitation, we shift the focus to the combination of time and frequency domains and propose a novel Hybrid Time-Frequency Dual-Branch Transformer for Sequential Recommendation, namely HyTiFRec. Specifically, we design two hybrid filter modules: the learnable… More >