Please wait a minute...
Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (3): 38-45    DOI: 10.11925/infotech.2096-3467.2017.03.05
Orginal Article Current Issue | Archive | Adv Search |
Personalized Recommendation Algorithm Based on Modified Tensor Decomposition Model
Chen Meimei(), Xue Kangjie
Glorious Sun School of Business & Management, Donghua University, Shanghai 200051, China
Download: PDF (742 KB)   HTML ( 27
Export: BibTeX | EndNote (RIS)      

[Objective] This paper tries to improve the prediction accuracy of personalized recommendation algorithm based on the tensor decomposition model. [Methods] First, we proposed a new tensor model using spectral clustering technique based on combined tag co-occurrence. Second, we established a penalty scheme on popular tag and resource co-occurrence with the help of IDF in TF-IDF. Finally,we re-defined the initial tensor on the triplets of user, tag cluster, and resource. [Results] We examined the proposed model with dataset from and found its precision, recall and F1 measure outperformed other algorithms. The F1 measures were increased by 5.91% and 1.29% thanks to the two proposed modifictions based on clustering and IDF. [Limitations] The proposed algorithm should be further evaluated with datasets from Weibo, Delicious, and other resources. [Conclusions] The new algorithm based on advanced tensor decomposition model could significantly improve the accuracy of resources recommendation to satisfy social network system users’ information needs.

Key wordsPersonalized Recommendation      UGC      Tag      Tag Co-occurrence      Spectral Clustering      Tensor Decomposition     
Received: 10 November 2016      Published: 25 September 1985
ZTFLH:  F224.39 TP391 TP181  

Cite this article:

Chen Meimei,Xue Kangjie. Personalized Recommendation Algorithm Based on Modified Tensor Decomposition Model. Data Analysis and Knowledge Discovery, 2017, 1(3): 38-45.

URL:     OR

数据集 准确性指标 CoSCluIDF CoSClu KmeansIDF TD
Tag8 Precision 22.69% 22.56% 22.38% 11.39%
Recall 43.61% 43.36% 42.94% 21.32%
F1 28.21% 28.05% 27.80% 13.99%
Tag20 Precision 23.67% 23.05% 22.54% 12.20%
Recall 45.80% 44.59% 43.59% 23.05%
F1 29.48% 28.71% 28.07% 15.03%
[1] Moens M F, Li J, Chua T S.Mining User Generated Content[M]. CRC Press, 2014: 7-9.
[2] Marinho L B, Nanopoulos A, Schmidt-Thieme L, et al.Social Tagging Recommender Systems[M]. USA: Springer US, 2011: 615-644.
[3] Hitchcock F L.The Expression of a Tensor or a Polyadic as a Sum of Products[J]. Journal of Mathematics & Physics, 1927, 6(1): 164-189.
doi: 10.1002/sapm192761164
[4] Symeonidis P, Nanopoulos A, Manolopoulos Y.Tag Recommendations Based on Tensor Dimensionality Reduction[C]//Proceedings of the 2008 ACM Conference on Recommender Systems, Lausanne, Switzerland. ACM, 2008: 43-50.
[5] 廖志芳, 王超群, 李小庆, 等. 张量分解的标签推荐及新用户标签推荐算法[J]. 小型微型计算机系统, 2013, 34(11): 2472-2476.
doi: 10.3969/j.issn.1000-1220.2013.11.011
[5] (Liao Zhifang, Wang Chaoqun, Li Xiaoqing, et al.Tag Recommendation and New User Tag Recommendation Algorithms Based on Tensor Decomposition[J]. Journal of Chinese Computer Systems, 2013, 34(11): 2472-2476.)
doi: 10.3969/j.issn.1000-1220.2013.11.011
[6] Rendle S, BalbyMarinho L, Nanopoulos A, et al. Learning Optimal Ranking with Tensor Factorization for Tag Recommendation[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2009: 727-736.
[7] 武慧娟, 徐宝祥, 王艳艳. 基于张量分解的个性化信息推荐方法优化研究[J]. 情报科学, 2014, 32(6): 134-137.
[7] (Wu Huijuan, Xu Baoxiang, Wang Yanyan.Optimization Research of Personalized Tag Recommendation Method Based on Tensor Decomposition[J]. Information Science, 2014, 32(6): 134-137.)
[8] Celma S, Cano P.From Hits to Niches? or How Popular Artists Can Bias Music Recommendation and Discovery[C]// Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition, Las Vegas, Nevada. ACM, 2008: 1-8.
[9] Salton G, Buckley C.Term-weighting Approaches in Automatic Text Retrieval[J]. Information Processing & Management an International Journal, 1988, 24(5): 513-523.
doi: 10.1016/0306-4573(88)90021-0
[10] Fleder D, Hosanagar K.Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity[J]. Management Science, 2007, 55(5): 697-712.
[11] 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.
[11] (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.)
[12] Cantador I, Bellogín A, Vallet D.Content-based Recommendation in Social Tagging Systems[C]// Proceedings of the 4th ACM Conference on Recommender Systems, Barcelona, Spain. ACM, 2010: 237-240.
[13] 项亮. 推荐系统实践[M]. 人民邮电出版社, 2012: 107-108.
[13] (Xiang Liang.Practice of Recommendation System[M]. Posts & Telecom Press, 2012: 107-108.)
[14] Rafailidis D, Daras P.The TFC Model: Tensor Factorization and Tag Clustering for Item Recommendation in Social Tagging Systems[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2013, 43(3): 673-688.
doi: 10.1109/TSMCA.2012.2208186
[15] Gemmell J, Ramezani M, Schimoler T, et al.The Impact of Ambiguity and Redundancy on Tag Recommendation in Folksonomies[C]//Proceedings of the 3rd ACM Conference on Recommender Systems, New York. ACM, 2009: 45-52.
[16] Leginus M, Dolog P, Žemaitis V.Improving Tensor Based Recommenders with Clustering[C]//Proceedings of the 20th International Conference on User Modeling, Adaptation, and Personalization, Montreal, Canada. Springer-Verlag, 2012: 151-163.
[17] Symeonidis P.ClustHOSVD: Item Recommendation by Combining Semantically Enhanced Tag Clustering with Tensor HOSVD[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 46(9): 1-12.
doi: 10.1109/TSMC.2015.2482458
[18] Shepitsen A, Gemmell J, Mobasher B, et al.Personalized Recommendation in Social Tagging Systems Using Hierarchical Clustering[C]//Proceedings of the 2008 ACM Conference on Recommender Systems, Lausanne, Switzerland. ACM, 2008: 259-266.
[19] Li H, Hu X, Lin Y, et al.A Social Tag Clustering Method Based on Common Co-occurrence Group Similarity[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(2): 122-134.
doi: 10.1631/FITEE.1500187
[20] 李瑞敏, 林鸿飞, 闫俊. 基于用户-标签-项目语义挖掘的个性化音乐推荐[J]. 计算机研究与发展, 2014, 51(10): 2270-2276.
doi: 10.7544/issn1000-1239.2014.20130342
[20] (Li Ruimin, Lin Hongfei, Yan Jun.Mining Latent Semantic on User-Tag-Item for Personalized Music Recommendation[J]. Journal of Computer Research and Development, 2014, 51(10): 2270-2276.)
doi: 10.7544/issn1000-1239.2014.20130342
[21] Symeonidis P, Nanopoulos A, Manolopoulos Y.A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis[J]. IEEE Transactions on Knowledge & Data Engineering, 2010, 22(2): 179-192.
doi: 10.1109/TKDE.2009.85
[22] Lathauwer L D, Moor B D, Vandewalle J.On the Best Rank-1 and Rank-(R1, R2,…, RN) Approximation of Higher-Order Tensors[J]. Siam Journal on Matrix Analysis & Applications, 2000, 21(4): 1324-1342.
[23] Kolda T G, Bader B W.Tensor Decompositions and Applications[J]. College & Research Libraries, 2005, 66(4): 294-310.
[24] Pazzani M, Billsus D.Learning and Revising User Profiles: The Identification of Interesting Web Sites[J]. Machine Learning, 1997, 27(3): 313-331.
doi: 10.1023/A:1007369909943
[25] White S, Smyth P.A Spectral Clustering Approach to Finding Communities in Graph[C]//Proceedings of the 2005 SIAM International Conference on Data Mining, Newport Beach, CA, USA. SIAM, 2005: 274-285.
[1] Ye Jiaxin,Xiong Huixiang,Tong Zhaoli,Meng Qiuqing. Collaborative Tagging for Doctors in Online Medical Community[J]. 数据分析与知识发现, 2020, 4(6): 118-128.
[2] Xiong Huixiang,Li Xiaomin,Li Yueyan. Group Recommendation Based on Attribute Mining of Book Reviews[J]. 数据分析与知识发现, 2020, 4(2/3): 214-222.
[3] Bocheng Li,Yunqiu Zhang,Kaixi Yang. Extracting Emotion Tags from Comments of Microblog Commodities[J]. 数据分析与知识发现, 2019, 3(9): 115-123.
[4] Lixin Xia,Jieyan Zeng,Chongwu Bi,Guanghui Ye. Identifying Hierarchy Evolution of User Interests with LDA Topic Model[J]. 数据分析与知识发现, 2019, 3(7): 1-13.
[5] Yiwen Zhang,Chenkun Zhang,Anju Yang,Chengrui Ji,Lihua Yue. A Conditional Walk Quadripartite Graph Based Personalized Recommendation Algorithm[J]. 数据分析与知识发现, 2019, 3(4): 117-125.
[6] Yue Yuan,Dongbo Wang,Shuiqing Huang,Bin Li. The Comparative Study of Different Tagging Sets on Entity Extraction of Classical Books[J]. 数据分析与知识发现, 2019, 3(3): 57-65.
[7] Jiaxin Ye,Huixiang Xiong. Recommending Personalized Contents from Cross-Domain Resources Based on Tags[J]. 数据分析与知识发现, 2019, 3(2): 21-32.
[8] Chongwu Bi,Guanghui Ye,Mingqian Li,Jieyan Zeng. Discovering City Profile Based on Tag Semantic Mining[J]. 数据分析与知识发现, 2019, 3(12): 41-51.
[9] Hao Ding,Shuqing Li. Personalized Recommendation Based on Predictive Analysis of User’s Interests[J]. 数据分析与知识发现, 2019, 3(11): 43-51.
[10] Wuxuan Jiang,Huixiang Xiong,Jiaxin Ye,Ning An. Creating Dynamic Tags for Social Networking Groups[J]. 数据分析与知识发现, 2019, 3(10): 98-109.
[11] Xiangdong Li,Fan Gao,Youhai Li. Categorizing Documents Automatically within Common Semantic Space[J]. 数据分析与知识发现, 2018, 2(9): 66-73.
[12] Jie Li,Fang Yang,Chenxi Xu. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[13] Guanghui Ye,Jinglan Hu,Jian Xu,Lixin Xia. Analyzing Growth Trends and Attachment Mode of Social Blog Tags[J]. 数据分析与知识发现, 2018, 2(6): 70-78.
[14] Wei Lu,Mengqi Luo,Heng Ding,Xin Li. Image Annotation Tags by Deep Learning and Real Users: A Comparative Study[J]. 数据分析与知识发现, 2018, 2(5): 1-10.
[15] Huixiang Xiong,Jiaxin Ye,Wuxuan Jiang. Clustering Social Tags with Improved DBSCAN Algorithm[J]. 数据分析与知识发现, 2018, 2(12): 77-88.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938