基于深度学习的会话推荐方法综述

Review of Deep Learning-based Conversation Recommendation Methods

随着信息技术和智能应用的迅速发展,现今社会面临着大量数据和信息过载的挑战。为解决这一难题,基于会话的推荐方法应运而生。基于深度学习的会话推荐方法利用其强大的表示学习能力,更准确地预测用户的短期兴趣,并提供个性化的推荐服务。故综述了深度学习会话推荐方法的研究进展,包括基于卷积神经网络、图神经网络、注意力机制、多层感知器、混合模型等方法。总结了研究难点和未来研究方向。随着深度学习技术的不断进步,会话推荐方法将在实际应用中发挥越来越重要的作用。

As information technology and intelligent applications continues to advance quickly,today’s society is facing challenges such as data and information overload.To solve this problem,conversation-based recommendation methods have emerged.Based on deep learning,these methods use their powerful representation learning ability to more accurately predict users’short-term interests and provide personalized recommendation services.This article reviews the research progress of deep learning-based conversation recommendation methods,including methods based on convolutional neural networks,graph neural networks,attention mechanisms,multi-layer perceptrons,and hybrid models.The article summarizes the research challenges and future directions.As deep learning technology continues to make progress,conversation-based recommendation methods are expected to become increasingly important in practical applications.