|Table of Contents|

Collaborative filtering recommendation algorithm based onuser interest and project cycle(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年04期
Page:
392-
Research Field:
Publishing date:

Info

Title:
Collaborative filtering recommendation algorithm based onuser interest and project cycle
Author(s):
Ye XijunYuan PeisenGou XiaoqingYan ZhihuiHe Jing
School of Information Sciences and Technology,Nanjing Agricultural University,Nanjing 210095,China
Keywords:
recommendation algorithm collaborative filtering individuation user interest project time user similarity item similarity linear fusion
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2018.42.04.002
Abstract:
Collaborative filtering recommendation algorithm takes the advantage of non-restricting recommended object type,without-user feedback information and so on,and stands out in many personalized recommendation algorithms. However,the existing algorithms don’t consider the differences between users and user interests,and the potential association between users and projects yet. These problems all affect the recommendation accuracy. This paper proposes the collaborative filtering recommendation algorithm based on user interest and project cycle,which introduces user interest weight UI,project time and other factors in the computation of similarity. This paper uses the fusion factor to integrate the improved user and project information to get the recommendation list. Finally,a comparison experiment shows that the proposed algorithm improves the recommendation accuracy by 11.034%. The research results show that the algorithm can effectively improve the accuracy of recommendation.

References:

[1] Chen Jianrui,Uliji,Wang Hua,et al. Evolutionary heterogeneous clustering for rating prediction based on user collaborative filtering[EB/OL]. Swarm and Evolutionary Computation,http://dx.doi.org/10.1016/j.swevo.2017.05.008.
[2]项亮. 推荐系统实践[M]. 北京:人民邮电出版社,2013.
[3]加那奇 D,占恩口 M,费尔琳 A,等. 推荐系统[M]. 蒋凡,译. 北京:人民邮电出版社,2013.
[4]刘青文. 基于协同过滤的推荐算法研究[D]. 中国科学技术大学计算机科学与工程学院,2013.
[5]Chen Hao,Li Zhongkun,Hu Wei. An improved collaborative recommendation algorithm based on optimized user similarity[J]. The Journal of Supercomputing,2016,72(7):2565-2578.
[6]Tarus J K,Niu Z,Yousif A. A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining[J]. Future Generation Computer Systems,2017,72:37-48.
[7]Panniello U,Tuzhilin A,Gorgoglione M. Comparing context-aware recommender systems in terms of accuracy and diversity[J]. User Modeling and User-Adapted Interaction,2014,24(1):35-65.
[8]Tarus J K,Niu Z,Yousif A. A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining[J]. Future Generation Computer Systems,2017,72:37-48.
[9]朱虹,李千目,戚湧. 一种基于改进最近邻算法的忠诚度预测方法[J]. 南京理工大学学报,2017,41(4):448-453.
Zhu Hong,Li Qianmu,Qi Yong. Loyalty prediction method based on improved nearest neighbor algorithm[J]. Journal of Nanjing University of Science and Technology,2017,41(4):448-453.
[10]Su Hongyi,Lin Xianfei,Yan Bo,et al. The collaborative filtering algorithm with time weight based on mapReduce[C]//International Conference on Big Data Computing and Communications. Taiyuan,China:Springer International Publishing,2015:386-395.
[11]Adibi P,Ladani B T. A collaborative filtering recommender system based on user’s time pattern activity[C]//Information and Knowledge Technology. Shiraz,Iran:IEEE,2013:252-257.
[12]Ponnam L T,Punyasamudram S D,Nallagulla S N,et al. Movie recommender system using item based collaborative filtering technique[C]//International Conference on Emerging Trends in Engineering,Technology and Science. Pudukkottai,India:IEEE,2016.
[13]Panniello U,Tuzhilin A,Gorgoglione M. Comparing context-aware recommender systems in terms of accuracy and diversity[J]. User Modeling and User-Adapted Interaction,2014,24(1):35-65.
[14]胡勋,孟祥武,张玉洁,等. 一种融合项目特征和移动用户信任关系的推荐算法[J]. 软件学报,2014,24(8):1817-1830.
Hu Xun,Meng Xiangwu,Zhang Yanjie,et al. Recommendation algorithm of combining item features and trust relationship of mobile users[J]. Journal of Software,2014,24(8):1817-1830.
[15]GroupLens Research. MovieLens Data sets[DB/OL]. https://grouplens.org/datasets/movielens/latest/,2017-08-15/2017-12-20.
[16]Herlockerzai J,Konstan J A,Riedl J. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms[J]. Information Retrieval Journal,2002,5(4):287-310.
[17]王成,朱志刚,张玉侠,等. 基于用户的协同过滤算法的推荐效率和个性化改进[J]. 小型微型计算机系统,2016,37(3):428-432.
Wang Cheng,Zhu Zhigang,Zhang Yuxia,et al. Improvement in recommendation efficiency and personalized of user-based collaborative filtering algorithm[J]. Journal of Chinese Computer Systems,2016,37(3):428-432.

Memo

Memo:
-
Last Update: 2018-08-30