[1]李 浩,梁京章,潘 莹 *.一种改进的兴趣相似度个性化推荐算法[J].计算机技术与发展,2022,32(12):1-6.[doi:10. 3969 / j. issn. 1673-629X. 2022. 12. 001]
　LI Hao,LIANG Jing-zhang,PAN Ying *.An Improved Personalized Recommendation Algorithm Based on Interest Similarity[J].,2022,32(12):1-6.[doi:10. 3969 / j. issn. 1673-629X. 2022. 12. 001]
点击复制

一种改进的兴趣相似度个性化推荐算法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年12期
页码:
1-6
栏目:
综述
出版日期:
2022-12-10

文章信息/Info

Title:
An Improved Personalized Recommendation Algorithm Based on Interest Similarity
文章编号:
1673-629X(2022)12-0001-06
作者:
李 浩1 ; 梁京章1 ; 潘 莹2 *
1. 广西大学 电气工程学院,广西 南宁 530004;
2. 广西大学 信息网络中心,广西 南宁 530004
Author(s):
LI Hao1 ; LIANG Jing-zhang1 ; PAN Ying2 *
1. School of Electrical Engineering,Guangxi University,Nanning 530004,China;
2. Information Network Center,Guangxi University,Nanning 530004,China
关键词:
推荐算法; 协同过滤; 兴趣相似度; 物品热点影响率; 物品属性满意度; 时间因子
Keywords:
recommendation algorithm; collaborative filtering; interest similarity; hot spot influence of articles; item attribute satisfaction; time factor
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 12. 001
摘要:
传统的协同过滤推荐算法在进行相似度计算时主要考虑用户对物品的评分,通过评分获取用户之间的相似度,缺少对用户兴趣相似度的考虑,同时在进行相似度计算时未考虑用户自身属性的影响,其相似度计算存在一定的失真性。针对这一问题,提出一种改进的兴趣相似度个性化推荐算法,根据不同的用户对物品的兴趣会因用户的自身属性不同而存在差别,设计一种改进的兴趣相似度计算方法,在进行兴趣相似度计算时引入用户的自身属性因素,如年龄、性别等属性因素;根据用户对物品的兴趣会受到物品的热门程度的影响,提出物品热点影响率与物品属性满意度的概念,并根据物品的热点影响率与物品属性满意度在计算相似度时赋予物品不同的权重关系;根据用户的兴趣会随着时间的变化而发生改变,将时间因素加入到推荐过程中,最终通过融合时间因子的影响做出最终的评分预测。 在 MovieLens 数据集上的实验结果表明,该算法的平均绝对误差( MAE) 和均方根误差( RMSE) 值更低,推荐效果更优。
Abstract:
The traditional collaborative filtering recommendation algorithm mainly considers the users’ rating of the item and obtains thesimilarity between users through the rating. It lacks the consideration of the users’ interest similarity,and does not consider the influenceof the users’ own attributes in the similarity calculation,so its similarity calculation has certain inauthenticity. To address this problem,wepropose an improved interest similarity personalized recommendation algorithm, and design an improved interest similarity calculationmethod based on the fact that different users’ interest in items may differ according to their own attributes, and introduce users’ ownattributes such as age and gender when calculating interest similarity. Based on the fact that users’ interest in items is affected by the popularity of items,we propose the concepts of item hotspot influence rate and item attribute satisfaction,and assign different weights to itemswhen calculating similarity based on their hotspot influence rate and item attribute satisfaction. Based on the fact that users ’ interestchanges with time,we add the time factor to the recommendation process,and finally make the final rating prediction by integrating theinfluence of the time factor. Experimental results on MovieLens dataset show that the average absolute error ( MAE) and root meansquare error ( RMSE) of the algorithm are lower,and the recommendation effect is better.

相似文献/References:

[1]邵延振 蒙韧 袁鼎荣 李新友.基于Web结构分区的协同过滤推荐算法研究[J].计算机技术与发展,2010,(06):67.
　SHAO Yan-zhen,MENG Ren,YUAN Ding-rong,et al.Collaborative Filtering Recommendation Algorithm Research Based on Web Blocks[J].,2010,(12):67.
[2]查文琴 梁昌勇 曹镭.基于用户聚类的协同过滤推荐方法[J].计算机技术与发展,2009,(06):69.
　ZHA Wen-qin,LIANG Chang-yong,CAO Lei.Collaborative Filtering Recommendation Method Based on Clustering of Users[J].,2009,(12):69.
[3]姜雅倩 王直杰 张珏.基于供求关系及协同过滤技术的推荐模型研究[J].计算机技术与发展,2007,(06):18.
　JIANG Ya-qian,WANG Zhi-jie,ZHANG Jue.Research on Recommendation Model Based on Supply and Demand Relation and Collaborative Filtering[J].,2007,(12):18.
[4]游文 叶水生.电子商务推荐系统中的协同过滤推荐[J].计算机技术与发展,2006,(09):70.
　YOU Wen,YE Shui-sheng.A Survey of Collaborative Filtering Algorithm Applied in E- commerce Recommender System[J].,2006,(12):70.
[5]徐红 彭黎 郭艾寅 徐云剑.基于用户多兴趣的协同过滤策略改进研究[J].计算机技术与发展,2011,(04):73.
　XU Hong,PENG Li,GUO Ai-yin,et al.User-Based Collaborative Filtering Strategies More Interested in Improvement of Research[J].,2011,(12):73.
[6]杨东风 牛永洁.基于混合规则的图书推荐模型设计与研究[J].计算机技术与发展,2011,(07):210.
　YANG Dong-feng,NIU Yong-jie.Books Recommended Model Design and Research Based on Mixing Rules[J].,2011,(12):210.
[7]吴月萍 王娜 马良.基于蚁群算法的协同过滤推荐系统的研究[J].计算机技术与发展,2011,(10):73.
　WU Yue-ping,WANG Na,MA Liang.Research of Collaboration Filtering Recommendation System Based on Ant Algorithm[J].,2011,(12):73.
[8]范虎,花伟伟.协同过滤推荐算法的研究与改进[J].计算机技术与发展,2013,(09):66.
　FAN Hu[],HUA Wei-wei[].Research and Improvement of Collaborative Filtering Recommendation Algorithm[J].,2013,(12):66.
[9]李振博,徐桂琼,査九. 基于用户谱聚类的协同过滤推荐算法[J].计算机技术与发展,2014,24(09):59.
　LI Zhen-bo,XU Gui-qiong,ZHA Jiu. A Collaborative Filtering Recommendation Algorithm Based on User Spectral Clustering[J].,2014,24(12):59.
[10]李荟,谢强,丁秋林. 一种基于情景的协同过滤推荐算法[J].计算机技术与发展,2014,24(10):42.
　LI Hui,XIEQiang,DING Qiu-lin. A Collaborative Filtering Recommendation Algorithm Based on Scenario[J].,2014,24(12):42.
[11]李克潮,蓝冬梅.一种属性和评分的协同过滤混合推荐算法[J].计算机技术与发展,2013,(07):116.
　LI Ke-chao,LAN Dong-mei.A Collaborative Filtering Hybrid Recommendation Algorithm for Attribute and Rating[J].,2013,(12):116.
[12]王全民,王莉,曹建奇. 基于评论挖掘的改进的协同过滤推荐算法[J].计算机技术与发展,2015,25(10):24.
　WANG Quan-min,WANG Li,CAO Jian-qi. Improved Collaborative Filtering Recommendation Algorithm Based on Comments Mining[J].,2015,25(12):24.
[13]李远博,曹菡. 基于PCA降维的协同过滤推荐算法[J].计算机技术与发展,2016,26(02):26.
　LI Yuan-bo,CAO Han. Collaborative Filtering Recommendation Algorithm Based on PCA Dimension Reduction[J].,2016,26(12):26.
[14]房旋[],陈升波[],宫婧[][],等. 基于社交影响力的推荐算法[J].计算机技术与发展,2016,26(06):31.
　FANG Xuan[],CHEN Sheng-bo[],GONG Jing[][],et al. A Recommendation Algorithm Based on Social Influence[J].,2016,26(12):31.
[15]蒋宗礼,王威,陆晨. 基于均值预估的协同过滤推荐算法改进[J].计算机技术与发展,2017,27(05):1.
　JIANG Zong-li,WANG Wei,LU Chen. 基于均值预估的协同过滤推荐算法改进[J].,2017,27(12):1.
[16]刘 旭,李玲娟.基于项目聚类和时间衰减的动态协同过滤算法[J].计算机技术与发展,2020,30(08):22.[doi:10. 3969 / j. issn. 1673-629X. 2020. 08. 004]
　LIU Xu,LI Ling-juan.Dynamic Collaborative Filtering Algorithm Based on Item Clustering and Time Decay[J].,2020,30(12):22.[doi:10. 3969 / j. issn. 1673-629X. 2020. 08. 004]
[17]赵学健,张雨豪,陈 昊,等.基于 FCM 用户聚类的协同过滤推荐算法[J].计算机技术与发展,2021,31(08):6.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 002]
　ZHAO Xue-jian,ZHANG Yu-hao,CHEN Hao,et al.Collaborative Filtering Recommendation Algorithm Based on Fuzzy C-Means User Clustering[J].,2021,31(12):6.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 002]
[18]殷佳莉,江智威,杨 毅,等.融合时间衰减函数的改进协同过滤算法[J].计算机技术与发展,2022,32(04):170.[doi:10. 3969 / j. issn. 1673-629X. 2022. 04. 029]
　YIN Jia-li,JIANG Zhi-wei,YANG Yi,et al.An Improved Collaborative Filtering Algorithm Incorporating Time Decay Function[J].,2022,32(12):170.[doi:10. 3969 / j. issn. 1673-629X. 2022. 04. 029]
[19]黄巧文,周宽久,费 铮,等.基于模糊聚类的多视图协同过滤推荐算法[J].计算机技术与发展,2023,33(08):14.[doi:10. 3969 / j. issn. 1673-629X. 2023. 08. 003]
　HUANG Qiao-wen,ZHOU Kuan-jiu,FEI Zheng,et al.Fuzzy Clustering Based Multi-view Collaborative Filtering Recommendation Algorithm[J].,2023,33(12):14.[doi:10. 3969 / j. issn. 1673-629X. 2023. 08. 003]

更新日期/Last Update: 2022-12-10