Fuxi Zhu1, Jin Xie2,*, Mohammed Alshahrani3
CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 949-971, 2024, DOI:10.32604/cmc.2024.051046
Abstract User representation learning is crucial for capturing different user preferences, but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated data, and thus cannot be measured directly. Text-based data models can learn user representations by mining latent semantics, which is beneficial to enhancing the semantic function of user representations. However, these technologies only extract common features in historical records and cannot represent changes in user intentions. However, sequential feature can express the user’s interests and intentions that change time by time. But the sequential recommendation… More >
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