计算机应用研究2018，Vol.35Issue(3)：717-720,802,5.DOI:10.3969/j.issn.1001-3695.2018.03.016
基于内容和兴趣漂移模型的电影推荐算法研究
Research on movie recommendation algorithm based on content and interest drift model
摘要
Abstract
Aiming at the problem of content-based recommendation algorithm,such as low accuracy of content similarity and interest drift of users,this paper proposed a recommendation method combing similarity of movie reviews plus short-term and long-term user interest model to calculate the movie similarity.This method used TextRank,Word2Vec and other models to extract the keywords and built the word vector.At the same time,it used the training results of the Word2Vec to calculate the movie content similarity,in this way,the low accuracy caused by the synonyms and Internet vocabulary could be solved to a certain extent.And then,based on the long-short-term interest drift model,it calculated the weights of user's preference content,and dynamically built movie similarity matrix according to the time window,to alleviate the problem of user interest drifting with time.Finally,it obtained the recommended results according to different recommendation strategies.The experimental results show that the proposed method improvcds the accuracy about 5％.Meantime,the interest model extracting the longshort-term interest tags of users,has high practical value in industry and also in the tag-based algorithms.关键词
个性推荐/词向量模型/用户偏好/兴趣漂移/聚类/集合相似度Key words
recommendation/word vector model/user preference/interest drifting/cluster/collection of similarity分类
信息技术与安全科学引用本文复制引用
吕学强,王腾,李雪伟,董志安..基于内容和兴趣漂移模型的电影推荐算法研究[J].计算机应用研究,2018,35(3):717-720,802,5.基金项目
国家自然科学基金资助项目(61271304,61671070) （61271304,61671070）
北京成像技术高精尖创新中心项目(BAICIT-2016003) （BAICIT-2016003）
国家社会科学基金重大项目(14@ZH036,15ZDB017) （14@ZH036,15ZDB017）
国家语委重大课题项目(ZDA125-26) （ZDA125-26）
