Residual Network Deep Learning Recommendation Model Based on Multi-head Attention Mechanism
ZHANG Yuanmeng
LI Shaobo
ZHOU Peng
YANG Mingbao
Abstract：Due to its strong feature expression ability,deep learning is gradually widely used in the field of recommendation research.DIN(Deep Interest Network)is a deep learning model for recommendation based on attention mechanism and user inter-est.Aiming at the problems of low completeness of feature training and improvement of recommendation accuracy,MHAR-DIN(Multi-Head Attention Residual Deep Interest Network)is proposed,which is an improved DIN based deep learning recommenda-tion model integrating multi-head attention module and residual network.The multi-head attention module is used to score the atten-tion based on the user's historical behavior,and the user's interest preference is fully considered.The residual network structure is introduced to directly connect the features across the training to the full connector,so as to solve the problem that it is difficult to train in too deep network.The comparative experiment between the proposed model and the classical deep learning recommendation model on the public data set movielens shows that the proposed MHAR-DIN model is effective and feasible.
Keywords：multi-head attention mechanismresidual networkrecommendation algorithmDINdeep learning
Publication Date：2024-07-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1955-1958,1965 )