计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 177-182.doi: 10.11896/jsjkx.191000141
耿蕾蕾1, 崔超然1, 石成2, 申朕2, 尹义龙3, 冯仕红1
GENG Lei-lei1, CUI Chao-ran1, SHI Cheng2, SHEN Zhen2, YIN Yi-long3, FENG Shi-hong1
摘要: 随着在线社交的多媒体共享网站的蓬勃发展社交图像推荐逐渐成为研究热点.人们通常对社交图像进行标签化、分组化使得图像数据更加易于管理.传统的图像标签或分组推荐方法往往只关注特定任务忽略了标签推荐和分组推荐任务之间的隐含关系.多任务学习则可以充分挖掘不同任务对图像的共享或相互关联的隐含表示融合多种任务抽取图像特征对于提高单一任务的准确性具有积极意义.因此文中提出了一种基于深度多任务学习的社交图像标签和分组联合推荐模型.该方法使用基于比较的偏序学习深度网络分别进行标签推荐和分组推荐有效缓解了单任务中的数据稀疏性问题.此外在处理社交图像视觉特征的卷积神经网络中首先使用多任务学习将来自不同任务的中间层特征进行连接然后通过卷积实现降维和特征的自动融合使得不同任务的图像特征得到共享同时降维后的融合特征能够满足下一层卷积神经网络的尺寸要求使得单一任务的整体结构得以保持.从大量Flickr图片共享网站上爬取的真实数据集上的实验结果表明与现有经典推荐算法相比所提算法获得的准确率和召回率均有较大提升证明了该方法的有效性和可行性.
中图分类号:
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