【作者】 王杰；

【导师】 江春华；

【作者基本信息】 电子科技大学 ， 软件工程， 2021， 硕士

【摘要】 对于推荐系统来说,数据稀疏和用户冷启动问题是重要的挑战之一。面对数据稀疏问题,常见的解决方案是使用跨领域推荐方法,从目标领域之外的其他辅助领域收集数据提升目标领域数据的密度,但假如某些用户在目标领域有着丰富的交互数据,而在辅助领域数据匮乏,这种传统的跨领域推荐方法便不能提升辅助领域数据的密度。针对这个问题,在基于领域间重叠用户场景下,本论文提出了基于多任务学习技术之“十字绣”网络的多领域推荐方法,该方法不仅能提升目标领域推荐的准确度,而且还能提升辅助领域推荐的准确度。当新用户进入系统时,推荐系统因缺少该用户兴趣信息而不能产生精准的推荐结果。为了解决用户冷启动问题,本文借鉴MV-DNN将用户向量和物品向量映射到共享语义空间,然后计算用户与物品相似度的思想,提出了一种基于深度神经网络的跨领域推荐方法,使用深度神经网络,将辅助领域的用户兴趣迁移到目标领域,解决目标领域用户冷启动问题。本论文的主要工作如下:1)在单领域内,将隐式行为数据转换成评分,然后填充到推荐系统已有的显式评分矩阵中。然后从评分矩阵中提取出用户向量和物品向量。2)提出了一种基于多任务学习的多领域推荐算法,使用多任务学习技术共享学习各个领域用户兴趣,丰富各个领域的用户向量,提升各个领域推荐的准确度。3)提出了一种基于深度神经网络跨领域推荐算法,先将各个领域提取出的用户向量进行合并,再将合并后的用户向量和各个领域的物品向量映射共享语义空间,以此解决用户冷启动问题。4)基于IPTV场景,实现了本论文中提出的算法,用真实环境数据进行测试。实验表明,本论文所提出的两种算法在提升推荐准确率和解决用户冷启动问题上都达到了良好的性能。更多还原

【Abstract】 For recommendation systems,data sparsity and user cold-start is one of the important challenging problems.Facing the problem of data sparsity,a common solution is to use cross-domain recommendation methods to collect data from other auxiliary domains outside the target domain to increase the density of the target domain data,but if some users who are in the lack of data in the auxiliary domain have rich interactive data in the target domain,this traditional cross-domain recommendation method cannot increase the density of the auxiliary domain data.For this problem,this thesis proposes a multi-domain recommendation method using multi-task learning technology that is cross-stitch network based on overlapping user scenarios between domains.This method can not only improve the accuracy of the target domain recommendation,but also improve the accuracy of the auxiliary domain recommendation.When user uses the system for the first time,the recommendation system cannot produce accurate recommendation results because of the lack of user interest information.In order to solve the user cold-start problem,we use MV-DNN to map the user vector and item vector to the shared semantic space,and then calculates the similarity between the user and the item,and proposes a cross-domain recommendation method based on a deep neural network,that is,using deep neural network transfers user interests in the auxiliary field to the target field,and solves the cold-start problem of users in the target field.The main work of this thesis is as follows:1)In a single domain,the implicit behavior data is converted into a score,and then filled into the existing display score matrix of the recommendation system.And the extract the user vector and item vector from the rating matrix.2)A multi-domain recommendation algorithm based on multi-task learning is proposed,which uses multi-task learning technology to share user interests in various fields,enrich user vectors in various fields,and improve the accuracy of recommendations in various fields.3)A cross-domain recommendation algorithm based on deep neural networks is proposed.The user vectors extracted from various fields are merged first,and the merged user vectors are mapped to the item vectors in each field to share the semantic space to solve the user problem.Cold start problem.4)Based on the IPTV scenario,the algorithm proposed in this thesis is implemented and tested with real environment data.Experiments show that the two algorithms proposed in this thesis achieve good performance in improving the recommendation accuracy and solving the user’s cold start problem.更多还原