[1]LIANG Lijun,LI Yegang,ZHANG Nana,et al.Collaborative filtering algorithm combining user features and preferences in optimized clustering[J].CAAI Transactions on Intelligent Systems,2020,15(6):1091-1096.[doi:10.11992/tis.201710024]
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Collaborative filtering algorithm combining user features and preferences in optimized clustering

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 6 Page number: 1091-1096 Column: 学术论文—人工智能基础 Public date: 2020-11-05
Title:
Collaborative filtering algorithm combining user features and preferences in optimized clustering
Author(s):
LIANG Lijun;  LI Yegang;  ZHANG Na’na;  ZHANG Xiao;  WANG Dong
College of Computer Science and Technology, Shandong University of Technology, Zibo 255049, China
Keywords:
recommendation system; collaborative filtering; cold start; scalability; optimization clustering; trust degree; user attribute; user preference
CLC:
TP311
DOI:
10.11992/tis.201710024
Abstract:
The collaborative filtering recommendation algorithm in the field of recommendation systems is still accompanied by the data sparsity, cold start, and scalability problems. To solve the cold start and scalability problems, we propose a PCEDS(pearson correlation coefficient and euclidean distance) collaborative filtering recommendation algorithm based on optimized clustering. First, the optimized K-means clustering algorithm is used to cluster the attributes of users. Then, based on the trust-based similarity model of user attribute features and the similarity model of user preference, a novel PCEDS similarity model is established to create a prediction model for the clustering results. The experimental results indicate that, compared with the traditional collaborative filtering recommendation algorithm, the proposed PCEDS collaborative filtering recommendation algorithm reduces the root mean square error by approximately 5%, significantly improves the recommendation precision and recall, and solves the cold start problem. Simultaneously, the clustering technology can save the memory space of the recommendation system, thereby improving its efficiency.
References:
[1] 王国霞, 刘贺平. 个性化推荐系统综述[J]. 计算机工程与应用, 2012, 48(7): 66-76
WANG Guoxia, LIU Heping. Survey of personalized recommendation system[J]. Computer engineering and applications, 2012, 48(7): 66-76
[2] Jamali M, Ester M. A transitivity aware matrix factorization model for recommendation in social networks[C]//Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Barcelona, Spain,2011:2644-2649.
[3] WEI Jian, HE Jianhua, CHEN Kai, et al. Collaborative filtering and deep learning based recommendation system for cold start items[J]. Expert systems with applications, 2017, 69: 29-39.
[4] AGGARWAL C C. An introduction to data mining[M]//AGGARWAL C C. Data Mining: the Textbook. Cham: Springer, 2015: 1-26.
[5] 孙天昊, 黎安能, 李明, 等. 基于Hadoop分布式改进聚类协同过滤推荐算法研究[J]. 计算机工程与应用, 2015, 51(15): 124-128
SUN Tianhao, LI Anneng, LI Ming, et al. Study on distributed improved clustering collaborative filtering algorithm based on Hadoop[J]. Computer engineering and applications, 2015, 51(15): 124-128
[6] 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, 25(8): 1817-1830
HU Xun, MENG Xiangwu, ZHANG Yujie, et al. Recommendation algorithm combing item features and trust relationship of mobile users[J]. Journal of software, 2014, 25(8): 1817-1830
[7] LIU Haifeng, HU Zheng, MIAN A, et al. A new user similarity model to improve the accuracy of collaborative filtering[J]. Knowledge-based systems, 2014, 56: 156-166.
[8] 韦素云, 肖静静, 业宁. 基于联合聚类平滑的协同过滤算法[J]. 计算机研究与发展, 2013, 50(S2): 163-169
WEI Suyun, XIAO Jingjing, YE Ning. Collaborative filtering algorithm based on co-clustering smoothing[J]. Journal of computer research and development, 2013, 50(S2): 163-169
[9] LEE D D, SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755): 788-791.
[10] ELKAHKY A M, SONG Yang, HE Xiaodong. A multi-view deep learning approach for cross domain user modeling in recommendation systems[C]//Proceedings of the 24th International Conference on World Wide Web. Florence, Italy, 2015: 278-288.
[11] 陈克寒, 韩盼盼, 吴健. 基于用户聚类的异构社交网络推荐算法[J]. 计算机学报, 2013, 36(2): 349-359
CHEN Kehan, HAN Panpan, WU Jian. User clustering based social network recommendation[J]. Chinese journal of computers, 2013, 36(2): 349-359
[12] 张顺龙, 库涛, 周浩. 针对多聚类中心大数据集的加速K-means聚类算法[J]. 计算机应用研究, 2016, 33(2): 413-416
ZHANG Shunlong, KU Tao, ZHOU Hao. Accelerate K-means for multi-center clustering of big datasets[J]. Application research of computers, 2016, 33(2): 413-416
[13] 贾洪杰, 丁世飞, 史忠植. 求解大规模谱聚类的近似加权核K-means算法[J]. 软件学报, 2015, 26(11): 2836-2846
JIA Hongjie, DING Shifei, SHI Zhongzhi. Approximate weighted kernel K-means for large-scale spectral clustering[J]. Journal of software, 2015, 26(11): 2836-2846
[14] DRAISMA J, HOROBET E, OTTAVIANI G, et al. The Euclidean distance degree of an algebraic variety[J]. Foundations of computational mathematics, 2016, 16(1): 99-149.
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