基于情感分析和LDA主题模型的协同过滤推荐算法  

Collaborative Filtering Recommendation Based on Sentiment Analysis and LDA Topic Model

作　　者：彭敏[1] 席俊杰[1] 代心媛 何炎祥[1] 

机构地区：[1]武汉大学计算机学院,湖北武汉430072

出　　处：《中文信息学报》2017年第2期194-203,共10页Journal of Chinese Information Processing

基　　金：国家自然科学基金(61472291;61303115)

摘　　要：协同过滤推荐算法通常基于物品或用户的相似度来实现个性化推荐,但是数据的稀疏性往往导致推荐精度不理想。大多数传统推荐算法仅考虑用户对物品的总体评分,而忽略了评论文本中用户对物品各个属性面的偏好。该文提出一种基于情感分析的推荐算法SACF(reviews sentiment analysis for collaborative filtering),该算法在经典的协同过滤推荐算法的基础上,考虑评论文本对相似度计算的影响。SACF算法利用LDA主题模型挖掘物品潜在的K个属性面,通过用户在各个属性面上的情感偏好计算用户相似度,从而构建推荐模型。基于京东网上评论数据集的实验结果表明,SACF算法不但可以有效地改善传统协同过滤推荐算法中数据稀疏性的问题,而且提高了推荐系统的精度。Collaborative Siltering achieves personalized recommendation based on the similarity between items or users. However, the data sparseness affects the calculation of similarity, leading to a low recommendation accuracy. Most of the traditional recommendation algorithms only consider the rate matrix between users and items, while ignoring the item reviews generated by users, that offer valuable information about the user＇s preferences to different attributes of the items. In this paper, we proposed a novel recommendation algorithm, called SACF （sentiment a- nalysis collaborative filtering）, which considers the impact of the review texts on the prediction of final score of items. By incorporating LDA topic model, SACF can extract K latent attribute aspects of the items and compute the user similarity according to the sentiment tendency in such attribute aspects. Our experimental results on Jingdong review dataset demonstrate that, the proposed method can not only alleviates the problem of data sparseness in col laborative filtering scheme, but also improves the reeommendation aceuracv.

关 键 词：推荐系统 协同过滤 LDA 情感分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]