Jianwei Zhang^1,2,*, Zhishang Zhao³, Zengyu Cai³, Yuan Feng⁴, Liang Zhu³, Yahui Sun³

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 977-996, 2025, DOI:10.32604/cmc.2024.058438 - 03 January 2025

Abstract The rapid development of short video platforms poses new challenges for traditional recommendation systems. Recommender systems typically depend on two types of user behavior feedback to construct user interest profiles: explicit feedback (interactive behavior), which significantly influences users’ short-term interests, and implicit feedback (viewing time), which substantially affects their long-term interests. However, the previous model fails to distinguish between these two feedback methods, leading it to predict only the overall preferences of users based on extensive historical behavior sequences. Consequently, it cannot differentiate between users’ long-term and short-term interests, resulting in low accuracy in describing… More >

Weihua Liu¹, Haoyang Wan^2,*, Boyuan Yan²

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 239-258, 2023, DOI:10.32604/cmes.2022.022827 - 29 September 2022

Abstract With the popularity of 5G and the rapid development of mobile terminals, an endless stream of short video software exists. Browsing short-form mobile video in fragmented time has become the mainstream of user’s life. Hence, designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements. Nevertheless, the explosive growth of data leads to the low efficiency of the algorithm, which fails to distill users’ points of interest on one hand effectively. On the other hand, integrating user preferences and the content of items urgently… More > Graphic Abstract