基于图过滤的快速密度聚类双层网络推荐算法

Double layered recommendation algorithm based on fast density clustering with graph-based filtering & Applications

信息过载问题使得推荐系统迅速发展并广泛应用,同时也出现不法商家将虚假消费记录定量地输入到系统数据库从而改变推荐系统的推荐结果以获利.因此,本文围绕3个问题展开,即:为了提高推荐系统对虚假评论的鉴别能力,首先需要准确标注虚假评论的类标,如何能获取大量准确标定的虚假评论信息;如何有效过滤虚假评论从而提高推荐的可靠性;如何实现一种高效可靠的推荐系统.针对虚假评论信息难以准确标定,本文提出了一种基于文本生成式对抗网络的自动点评技术,依据历史评论文本自动生成虚假评论文本,并依据情感分析确定生成文本的对应评分;为了提高推荐系统对包含虚假信息数据的推荐效果,本文提出了一种基于图过滤的快速密度聚类双层网络推荐算法.该算法首先提出了一种能快速确定节点执行度阈值的基于图的过滤器,有效过滤数据内虚假信息,并设计了一种快速密度聚类双层网络推荐算法,提高推荐效果.将所提出的推荐算法应用到Yelp数据集上展开试验,验证本文提出的推荐方法的有效性.

The information overloading problem leads to wider application of recommender system. At the meantime,fake reviewers are quantitative input into the history review records by illegal business to affect the recommender to change for their benefits. Three research questions are addressed in our paper. In order to improve fake review filtering ability for recommenders, abundant of accurately labeled fake reviewers are necessary. How to collect large amount of accurately labeled fake reviewers? How to filter fake reviewers accurately and efficiently? How to design an efficient recommender?Since it’s difficult to collect labeled fake reviewers, an automatic reviewer generator based on text generative adversarial nets is proposed. Reviewers labeled as fake can be generated based on historical reviewers and can be rated according to emotional analysis. In order to improve the recommendation effect of containing false information data, this paper proposes a double layered recommendation algorithm based on fast density clustering and graph-based filtering. Firstly, we design a graph-based filter that can quickly determine node execution thresholds to effectively filters the false information. And a recommender based on fast clustering is put forward, which is a density based clustering algorithm with cluster center self-determined, to implement accurate recommendation. At last, the proposed algorithm is applied to the Yelp data set to verify its effectiveness.