首页 | 本学科首页   官方微博 | 高级检索  
     

协同过滤推荐算法研究: 考虑在线评论情感倾向
引用本文:王伟,王洪伟,孟园. 协同过滤推荐算法研究: 考虑在线评论情感倾向[J]. 系统工程理论与实践, 2014, 34(12): 3238-3249. DOI: 10.12011/1000-6788(2014)12-3238
作者姓名:王伟  王洪伟  孟园
作者单位:同济大学 经济与管理学院, 上海 200092
基金项目:国家自然科学基金(70971099,71371144);上海市哲学社会科学规划课题一般项目(2013BGL004);中央高校基本科研业务费专项资金(1200219198)
摘    要:协同过滤推荐算法通常是基于兴趣相似的用户行为来实现个性化推荐, 其核心义用户之间的兴趣相似度. 本文在传统的协同过滤推荐算法基础上, 考虑在线评论对用似度识别的影响. 在混合商品推荐中, 粗粒度识别评论情感极性; 而在同类商品推荐中, 细粒别每个商品特征的情感极性. 如果用户对产品的某个特征评价次数大于平均次数, 表明用户对征较关注; 如果对产品的某个特征评价低于平均评价, 表明用户对该特征较挑剔. 进而根据用户评论来建立用户偏好模型, 用户在评论中反映出来的相似度越高, 表明用户之间的偏好越. 实验表明, 同传统的协同过滤算法相比, 基于在线评论情感分析的用户协同过滤算法在率和召回率指标上有显著提升.

关 键 词:推荐系统  推荐算法  协同过滤  在线评论  情感分析  
收稿时间:2013-09-02

The collaborative filtering recommendation based on sentiment analysis of online reviews
WANG Wei,WANG Hong-wei,MENG Yuan. The collaborative filtering recommendation based on sentiment analysis of online reviews[J]. Systems Engineering —Theory & Practice, 2014, 34(12): 3238-3249. DOI: 10.12011/1000-6788(2014)12-3238
Authors:WANG Wei  WANG Hong-wei  MENG Yuan
Affiliation:School of Economics and Management, Tongji University, Shanghai 200092, China
Abstract:Collaborative filtering recommendation algorithm bases on user behavior with similar interests to produce personalized recommendation. The core of the algorithm is to define the distance between the user's interest similarities. The paper considers the online review sentiment impact on user similarity recognition. In mixed products recommendation, coarse-grained sentimental polarity is identified; while in same category products recommendation, fine-grained sentimental analysis is employed for each feature. If the user's evaluation frequency is greater than the average on a special feature, it indicates that the user pays close attention to the feature; while if the user's rating is smaller than the average rating on a special feature, it means the user has a strict requirement on this feature. And then the user's preference model is created according to reviews, the higher the similarity between users in the reviews, the more consistent preferences between users. Experiment results show that the proposed collaborative filtering algorithm based on sentiment analysis of online reviews improves the traditional algorithm significantly on accuracy and recall.
Keywords:recommendation system  recommendation algorithm  collaborative filtering  online review  sentiment analysis  
本文献已被 CNKI 等数据库收录!
点击此处可从《系统工程理论与实践》浏览原始摘要信息
点击此处可从《系统工程理论与实践》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号