基于增量协同过滤和潜在语义分析的混合推荐算法  

A hybrid recommendation model based on incremental collaborative filtering and latent semantic analysis

作　　者：刘辉[1,2,3] 万程峰 吴晓浩 LIU Hui;WAN Cheng-feng;WU Xiao-hao(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065;Research Center of New Telecommunication Technology Applications,Chongqing University of Posts and Telecommunications,Chongqing 400065;Chongqing Information Technology Designing Co.Ltd.,Chongqing 401121,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065 [2]重庆邮电大学通信新技术应用研究中心,重庆400065 [3]重庆信科设计有限公司,重庆401121

出　　处：《计算机工程与科学》2019年第11期2033-2039,共7页Computer Engineering & Science

摘　　要：为了解决传统新闻推荐系统定期更新推荐算法不能根据用户喜好的变化进而动态地调整推荐列表的问题,提出了一种混合推荐算法(IULSACF)。该算法包含了2个关键部分:基于项目的增量更新协同过滤算法和基于关键词频率的潜在语义分析算法。该混合推荐算法在基于项目的增量更新协同过滤模块中,通过对项目相似度列表增量更新来动态地调整推荐列表,并结合潜在语义分析算法来确保所推荐文章的相关性。实验结果表明,所提出的IULSACF算法在各项评价指标上均优于传统的推荐方法。Traditional news recommendation systems regularly update recommendation models,which cannot adjust recommendation lists dynamically according to the change of user preferences.In order to solve this problem,we propose a hybrid recommendation model(IULSACF).It includes two key parts:an item-based incremental update collaborative filtering algorithm and a key word frequency based latent sementic analysis algorithm.The hybrid recommendation model dynamically adjusts the recommendation list by incrementally updating the similarity list of items in the item-based incremental update collaborative filtering module,and combines the latent semantic analysis algorithm to ensure the relevance of recommended articles.Experimental results show that the proposed IULSACF algorithm is superior to traditional recommendation methods in all evaluation indexes.

关 键 词：新闻推荐 增量更新 协同过滤 潜在语义分析 项目相似度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]