计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 18-24.doi: 10.11896/jsjkx.210600126
齐秀秀, 王佳昊, 李文雄, 周帆
QI Xiu-xiu, WANG Jia-hao, LI Wen-xiong, ZHOU Fan
摘要： 随着互联网社交媒体规模的飞速发展,利用推荐算法对海量信息进行有效建模筛选和过滤,成为了研究用户行为偏好、热点倾向和网络安全态势等问题的关键。随着深度学习的发展,图神经网络模型在解决推荐系统应用中的密集型图结构数据时取得了较好效果。协同过滤算法作为得到最广泛应用的推荐算法,其利用用户-项目的群体交互数据来预测用户未来的偏好与项目评级。但现有的推荐算法仍面临着数据稀疏和冷启动问题,且缺少对不确定性的良好量化。文中提出了一种基于概率元学习的归纳矩阵补全预测融合算法(MetaIMC),该算法从贝叶斯推断的角度重新对元学习进行表征,构建了稳健的图深度神经网络元学习模型,充分利用数据先验知识提出从稀疏数据中学习新任务的解决方案。首先,MetaIMC可以有效地利用变分贝叶斯推理获得先验分布,缓解元模型任务训练中的不确定性和模糊性问题,进一步提升了模型的泛化能力;其次,在不借助任何用户边信息的情况下,实现新用户推荐的冷启动;最后,在传统矩阵补全及用户冷启动两个场景下,利用Flixster,Douban和Yahoo_music 3个公开数据集对模型的性能进行了评估,验证了MetaIMC在面对传统矩阵补全任务时的有效性,并在冷启动问题上达到了最优的效果。
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