南京大学学报(自然科学版) ›› 2022, Vol. 58 ›› Issue (1): 143152.doi: 10.13232/j.cnki.jnju.2022.01.014
• • 上一篇
Cong Wu, Wenming Ma(), Bing Wang, Jianhao Zhu
摘要:
随着互联网时代的到来,推荐系统已经成为人们在网络上筛选资源的得力助手,传统推荐系统通过用户的评分信息来计算用户相似度并为用户进行资源的推荐,但仍存在冷启动、数据稀疏性等各种问题,极大地影响推荐质量.近年来,标签的出现带给推荐系统新的机遇,它能够具体准确地描述用户的兴趣偏好,使推荐系统可以通过标签属性来更准确地了解用户喜好,从而为用户进行个性化推荐,极大提高了推荐精度和用户满意度.结合标签属性与评分的关系来计算用户标签相似度,结合用户和资源信息来计算用户相似度,将两者同时融入矩阵分解模型中,从而加强了推荐依据,提升了推荐的准确性.实验结果表明,在ml?latest?small数据集上,提出的算法UTagJMF的RMSE降低2%左右;在Hetrec2011?movielens?2k数据集上,UTagJMF的RMSE降低2.2%左右.证明提出的算法模型明显优于其他算法的预测效果.
中图分类号:
1 | 周万珍,曹迪,许云峰,等.推荐系统研究综述.河北科技大学学报,2020,41(01):76-87. |
Zhou W Z,Cao D,Xu Y F,et al.A survey ofrecommendation systems.Journal of Hebei Universityof Science and Technology,2020,41(1):76-87. | |
2 | 潘博磊. 5G网络新技术及核心网架构. 信息与电脑,2019(16):172-173,181. |
Pan B L. 5G network new technology and core network architecture. China Computer & Communication,2019(16):172-173,181. | |
3 | 李新卫. 基于Hadoop的音乐推荐系统的研究与实现. 硕士学位论文. 西安:西安工业大学,2018. |
Li X W. Research and implementation of music recommendation system based on Hadoop. Master Disser?tation. Xi'an:Xi'an Technological University,2018. | |
4 | 李卓远,曾丹,张之江. 基于协同过滤和音乐情绪的音乐推荐系统研究. 工业控制计算机,2018,31(7):130-131,134. |
Li Z Y,Zeng D,Zhang Z J. Research on music recommender systems based on collaborative filtering and music emotion. Industrial Control Computer,2018,31(7):130-131,134. | |
5 | 侯强. 基于在线评论的泛视频推荐系统的设计与实现.博士学位论文.大连:大连理工大学,2018. (Design and implementation of pan?video recommendation system based on online comments. Ph.D. Dissertation. Dalian:Dalian University of Technology,2018.) |
6 | Wang L C,Meng X W,Zhang Y J. Context?aware recommender systems. Journal of Software,2012,23(1):1-20. |
7 | Sarwar B,Karypis G,Konstan J,et al. Item?based collaborative filtering recommendation algorithms∥Proceedings of the 10th International Conference on World Wide Web. Hong Kong,China:ACM,2001:285-295. |
8 | Breese J S,Heckerman D,Kadie C. Empirical analysis of predictive algorithms for collaborative filtering∥Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Madison,WI,USA:ACM,1998:43-52. |
9 | Konstan J A,Miller B N,Maltz D,et al. GroupLens:Applying collaborative filtering to Usenet news. Communications of the ACM,1997,40(3):77-87. |
10 | Wei S X,Zheng X L,Chen D R,et al. A hybrid approach for movie recommendation via tags and ratings. Electronic Commerce Research & Applications,2016(18):83-94. |
11 | de Campos L M,Fernández?Luna J M,Huete J F,et al. Combining content?based and collaborative recommendations:A hybrid approach based on Bayesian networks. International Journal of Approximate Reasoning,2010,51(7):785-799. |
12 | Costeira J P,Kanade T. A multibody factorization method for independently moving objects. International Journal of Computer Vision,1998,29(3):159-179. |
13 | Lu L,Vidal R. Combined central and subspace clustering for computer vision applications∥Proceedings of the 23rd International Conference on Machine Learning. Pittsburgh,PE,USA:ACM,2006:593-600. |
14 | Xu R,Wunschii D. Survey of clustering algorithms. IEEE Transactions on Neural Networks,2005,16(3):645-678. |
15 | Ma H,Yang H X,Lyu M R,et al. SoRec:Social recommendation using probabilistic matrix factorization∥Proceedings of the 17th ACM Conference on Information and Knowledge Management. Napa Valley,CA,USA:ACM,2008:931-940. |
16 | Koren Y. Collaborative filtering with temporal dynamics. Communications of the ACM,2010,53(4):89-97. |
17 | Gantner Z,Drumond L,Freudenthaler C,et al. Learning attribute?to?feature mappings for cold?start recommendations∥2010 IEEE International Conference on Data Mining. Sydney,Australia:IEEE,2010:176-185. |
18 | Zhao L,Xiao B. Matrix factorization based models considering item categories and user neighbors∥2015 8th International Symposium on Computational Intelligence and Design. Hangzhou,China:IEEE,2015:470-473. |
19 | 杨强,杨有,余平. 信任传递的矩阵分解推荐算法. 重庆文理学院学报,2015,34(5):125-129. |
Yang Q,Yang Y,Yu P. Martrix factorization recommender algorithm using trust propagation. Journal of Chong?qing University of Arts and Sciences,2015,34(5):125-129. | |
20 | Zhang K H,Liang J Y,Zhao X W,et al. A collaborative filtering recommendation algorithm based on information of community experts. Journal of Computer Research and Development,2018,55(5):968-976. |
21 | Yu Y H,Gao Y,Wang H,et al. Integrating user social status and matrix factorization for item recommendation. Journal of Computer Research and Development,2018,55(1):113-124. |
22 | 何明,要凯升,杨芃,等. 基于标签信息特征相似性的协同过滤个性化推荐. 计算机科学,2018,45(6A):415-422. |
He M,Yao K S,Yang P,et al. Collaborative filtering personalized recommendation based on similarity of tag information feature. Computer Science,2018,45(6A):415-422. | |
23 | 姚陶钧. 基于社会化标签和概率化矩阵分解推荐算法的研究. 硕士学位论文. 杭州:浙江大学,2013. |
Yao T J. Research on recommendation algorithm based on social tagging and probabilistic matrix factorization.Master Dissertation. Hangzhou:Zhejiang University, 2013. | |
24 | Zhen Y,Li W J,Yeumg D Y. TagiCoFi:Tag informed collaborative filtering∥Proceedings of the 3rd ACM Conference on Recommender Systems. New York,NY,USA:ACM,2009:69-76. |
25 | Diederich J,Iofciu T. Finding communities of practice from user profiles based on folksonomies. CEUR Workshop Proceedings,2006:213. |
26 | Heung?Nam K,Majdi R,Abdulmotaleb El S. Leveraging collaborative filtering to tag?based personalized search. Usex Modeling A daption and Personalization,2011:195-206. |
27 | Eck D,Lamere P,Bertin?Mahieux T,et al. Automatic generation of social tags for music recommendation∥Proceedings of the 20th Inter?national Conference on Neural Information Processing Systems. Vancouver,Canada:Curran Associates Inc.,2007:385-392. |
28 | Zhao S W,Du N,Naucrz A,et al. Improved recommendation based on collaborative tagging behaviors∥Proceedings of the 13th International Conference on Intelligent User Interfaces. Gran Canaria,Spain:ACM,2008:413-416. |
29 | Firan C S,Nejdl W,Paiu R. The benefit of using tag?based profiles∥Proceedings of 2007 Latin American Web Conference. Santiago,Chile:IEEE,2007:32-41. |
30 | 吴航. 融入用户信任和标签的协同过滤推荐研究. 硕士学位论文. 上海:华东师范大学,2019. |
Wu H. Research on collaborative filtering recommendation integrating user trust and tags. Master Dissertation. Shanghai:East China Normal University,2019. | |
31 | 王运,倪静. 融合用户偏好和物品相似度的概率矩阵分解推荐算法. 小型微型计算机系统,2020,41(4):746-751. |
Wang Y,Ni J. Probability matrix factorization recommendation algorithm combining user preferences and item similarity. Journal of Chinese Computer Systems,2020,41(4):746-751. |
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