基于深度学习的协同过滤模型研究

发布时间：2018-04-22 06:33

本文选题：栈式降噪自编码 + 协同过滤　；参考：《深圳大学》2017年硕士论文

【摘要】：随着互联网技术的日新月异,民众的生活方式发生了重大的改变。在信息琳琅满目、竞争激励的互联网时代,如何帮助用户快速准确的挑选出其感兴趣的物品,对一个互联网企业至关重要。基于上述问题,推荐系统技术应运而生。协同过滤技术是推荐系统中使用最广,最受欢迎的一项技术。传统协同过滤技术仅使用用户对物品的评分矩阵,但是通常情况下,评分矩阵非常稀疏,导致推荐系统的推荐准确率严重下降,并且传统协同过滤技术对于新物品还存在冷启动的问题。针对这些问题,本文的主要工作包括以下两个部分:(1)受限波尔兹曼机RBM用于协同过滤时,其推荐性能与评分矩阵的稀疏性有很大的关联,当评分矩阵稀疏时其推荐性能不佳,且基于RBM的推荐仅使用评分矩阵,对于新物品存在冷启动的问题。针对上述问题,本文提出一种结合物品内容相似性的RBM协同过滤方法,命名为CS-RBM。该方法利用Word2vec对物品内容进行向量表示,并计算物品之间的相似度,然后将所得到的物品间的相似度度量添加到RBM模型预测评分上,从而使最后预测出来的评分既考虑了评分矩阵中隐因子的影响,又考虑了物品内容之间相似度的影响。经在ml-100k、ml-1m、Netflix多个数据集上的实验结果表明,结合物品内容相似性的RBM协同过滤方法能比原始的RBM模型具有更好的推荐性能。(2)由于结合物品内容的RBM协同过滤方法仅简单利用了物品的内容信息,不能从物品的内容信息中捕获更深层次的隐因子用于模型改进,且没有考虑用户特征对模型的影响。针对这些问题,本文在深度协同模型CDL的基础上,提出了同时对用户特征和物品特征进行双向约束的深度协同模型DCDL。该模型同时利用深度栈式降噪自动编码SDAE和概率矩阵分解PMF协同训练,自动从物品内容和评分矩阵中学习物品隐藏特征和用户隐藏特征,使得模型既考虑了物品内容对推荐的影响,又考虑了用户特征对推荐的影响。经在citeulike-a、citeulike-t、Netflix多个数据集上的实验结果表明,DCDL相对于协同主题回归模型CTR和深度协同模型CDL具有更好的推荐性能。
[Abstract]:With the rapid development of Internet technology, people's way of life has changed greatly. In the era of the Internet, which is full of information and competition, how to help users quickly and accurately pick out the objects of interest is very important to an Internet enterprise. Based on the above problems, recommendation system technology emerged as the times require. Collaborative filtering is one of the most popular and widely used technologies in recommendation systems. The traditional collaborative filtering technology only uses the scoring matrix of the user to the item, but usually, the score matrix is very sparse, resulting in the recommendation accuracy of the recommendation system seriously reduced. And the traditional collaborative filtering technology has the problem of cold start for new items. In order to solve these problems, the main work of this paper includes the following two parts: 1) when RBM is used for collaborative filtering, its recommendation performance is closely related to the sparsity of the score matrix. When the score matrix is sparse, its recommendation performance is not good. The recommendation based on RBM only uses the score matrix, which has the problem of cold start for new items. In order to solve the above problems, this paper proposes a collaborative RBM filtering method, named CS-RBM, which combines the similarity of the content of articles. In this method, Word2vec is used to vector the content of the items, and the similarity between the items is calculated. Then, the similarity measure of the items is added to the prediction score of the RBM model. Therefore, the predicted score not only takes into account the influence of implicit factors in the scoring matrix, but also takes into account the influence of similarity between items. The results of experiments on multiple data sets on ml-100k/ m-1 / Netflix show that, The RBM collaborative filtering method combined with the similarity of article content has better recommended performance than the original RBM model. (2) because the RBM collaborative filtering method combined with the content of articles can only make use of the content information of the article simply. Further hidden factors can not be captured from the content information of the items for model improvement and the influence of user characteristics on the model is not considered. In order to solve these problems, this paper proposes a depth collaboration model based on the depth collaboration model (CDL), which simultaneously binds both user and item features. At the same time, the model uses depth stack denoising automatic coding SDAE and probability matrix factorization PMF cooperative training to automatically learn item hiding feature and user hiding feature from item content and score matrix. The model not only considers the effect of item content on recommendation, but also considers the influence of user characteristics on recommendation. The experimental results on several data sets show that DCDL has better performance than CTR and CDL.
【学位授予单位】：深圳大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.3

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