MIND Microblogging Recommendation Algorithm Based on Dual Attention Mechanism
PENG Dan
WEI Jiayin
LU Youjun
YAO Lin
WANG Qian
Abstract：Aiming at the current problem that in the microblogging recommendation field a single vector was mainly used to represent user interests and capture ability of the complex relationship between interests was lacking,which led to incomplete representation of user interests and low recommendation accuracy,a multi-interest network with dynamic routing microblogging recommendation algorithm based on dual attention mechanism(MINDDouAtt)was proposed to improve the characterization ability of user interests.It was proposed for improving the characterization of user interests.First,multiple interest capsules were extracted from user behavioral data through dynamic routing,and these interest capsules were fed into the self-attention mechanism to cross-learn the association information between different interest capsules to improve the characterization ability of interests.Then,the importance between different interest capsules was adjusted by introducing a label-aware attention mechanism to better meet users'personalized recommendation needs.The experiment showed that the proposed model performed well on Amazon Books,Tmall,and Weibo datasets.Compared with the best comparison model,the values of SHR@10 improved by 33.66％,10.49％,and 9.60％respectively.The MINDDouAtt algorithm can provide users with more accurate and personalized recommendation results in fields such as e-commerce.
Keywords：microblog recommendationmulti interest recalldynamic routinginterest capsulesself-attention mechanismslabel-aware attention mechanisms
Publication Date：2024-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 507-513 )