JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2015, Vol. 50 ›› Issue (09): 29-35.doi: 10.6040/j.issn.1671-9352.3.2014.232

Previous Articles     Next Articles

Hybrid recommendation by combining network-based algorithm and user preference

ZHANG Xin-meng1,2, JIANG Sheng-yi1,2, ZHANG Qian-sheng2,3, XIE Bo-lin1,2, LI Xia2   

  1. 1. Cisco School of Informatics, Guangdong University of Foreign Studies, Guangzhou 510006, Guangdong, China;
    2. Social Science Key Laboratory of Language Engineering and Computing of Guangdong Province, Guangzhou 510006, Guangdong, China;
    3. School of Finance, Guangdong University of Foreign Studies, Guangzhou 510006, Guangdong, China
  • Received:2015-03-03 Revised:2015-07-22 Online:2015-09-20 Published:2015-09-26

PDF (PC)

939

Abstract: Recommendation algorithms based on heat conduction or mass diffusion first obtain the relationship between objects according to network structure, then predict the user's favorite objects based on these relationships, but these algorithms ignore user's preference. In order to overcome this defect, the TF-IDF approach was used to construct user's preference according to the tags contained in the objects selected by user, and the mean of preference of object's tags was taken as the preference of the object, then a hybrid recommendation model was proposed by combining network-based algorithm and the user preference model. The benchmark datasets, MovieLens, was used to evaluate our algorithm, and the experimental results demonstrate that hybrid algorithm can significantly improve accuracy, diversification and personalization of recommendations.

Key words: TF-IDF, network-based recommendation, personalized recommendation, tag

CLC Number: 

  • TP393
[1] ZHOU T, REN J, MEDO M, et al. Bipartite network projection and personal recommendation[J]. Physical Review E, 2007, 76(4):6116-6123.
[2] ZHOU T, JIANG L L, SU R Q. Effect of initial configurationon network-based recommendation[J]. Europhys Lett, 2008, 81(5):8004-8008.
[3] PAN X, DENG G S, LIU J G. Weighted bipartite network and personalized recommendation[J]. Physics Procedia, 2010, 3(5):1867-1876.
[4] LIU J G, ZHOU T, WANG B H, et al. Effects of user tastes on personalized recommendation[J]. International Journal of Modern Physics C, 2009, 20(12):1925-1932.
[5] ZHOU T, SU R Q, LIU R R, et al. Accurate and diverse recommendations via eliminating redundant correlations[J]. New J Phys, 2009, 11(12):3008-3026.
[6] ZHANG Y C, BLATTNER M, YU Y K. Heat conduction process on community networks as a recommendation model[J]. Phys Rev Lett, 2007, 99(15):4301-4305.
[7] LIU J G, ZHOU T, GUO Q. Information filtering via biased heat conduction[J]. Phys Rev E, 2011, 84(3):7101-7105.
[8] QIU T, WANG T T, ZHANG Z K, et al. Heterogeneity involved network-based algorithm leads to accurate and personalized recommendations[J]. Physics and Society, 2013, arXiv:1305.7438vl.
[9] ZHOU T, KUSCSIK Z, LIU J G, et al. Solving the apparent diversity accuracy dilemma of recommender systems[C]//Proceedings of the National Academy of Sciences of the United States of America. Washington: Natl Acad Sciences, 2010, 107:4511-4515.
[10] SCOTT A G, BERNARDO A H. Usage patterns of collaborative tagging systems[J]. Journal of Information Science, 2006, 32(2):198-208.
[11] ZHANG Z K, ZHOU T, ZHANG Y C. Tag-aware recommender systems: a state-of-the-art survey[J]. Journal of Computer Science and Technology, 2011, 26(5):767-777.
[12] ZHANG Z K, LIU C, ZHANG Y C, et al. Solving the cold-start problem in recommender systems with social tags [J]. Europhysics Letters, 2010, 92(2):8002-8010.
[13] MICHAEL J P, DANIEL B. Content-based recommendation systems[J]. Lecture Notes in Computer Science, 2007, 4321:325-341.
[14] CANTADOR I, BELLOGN A, VALLET D. Content-based recommendation in social tagging systems[C]//Proceedings of RecSys'10.New York: ACM, 2010:237-240.
[15] JIANG Shengyi, SONG Xiaoyu, WANG Hui, et al. A clustering-based method for unsupervised intrusion detections[J]. Pattern Recognition Letters, 2006, 27(7):802-810.
[16] BURKE R. Hybrid Recommender systems: survey and experiments [J]. User Model User-Adap Interact, 2007, 12(4):331-370.
[17] JOACHIMS T. A probabilistic analysis of the rocchio algorithm with TFIDF for text categorization[C]//Proceedings of the 14th International Conference on Machine Learning. New York: ACM, 1997:143-151.
[1] DONG Xing-lin, QI Xin. Interactive relationship between two stages of technology input and output in Qingdao city based on time delay effect [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(5): 80-87.
[2] ZHANG Cong, PEI Jia-huan, HUANG Kai-yu, HUANG De-gen, YIN Zhang-zhi. Semantic graph optimization algorithm based chinesemicroblog opinion summarization [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 59-65.
[3] SUN Shi-chang, LIN Hong-fei, MENG Jia-na, LIU Hong-bo. Model selection with likelihood ratio for sequence transfer learning [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(6): 24-31.
[4] YANG Zhen, SI Shu-yong, LI Chao-yang. Information recommendation based on users interest model [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(1): 15-22.
[5] LIU Kun-lun. Application of variable structure pair copula model in the analysis of financial contagion [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(6): 104-110.
[6] WU Huan, ZHAN Jing, ZHAO Yong, TAO Zheng, YANG Jing. An efficient multilevel interconnection network security mechanism based on virtualization [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(3): 98-103.
[7] HE Xin-hua, HU Wen-fa, XU Chang-yan, CHEN Ji-hong. Collaborative transshipment strategy of service supply chain for emergencies based on stochastic and fuzzy simulation [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(12): 67-77.
[8] ZHANG Shao-qun, WEI Jing-jing, LIAO Xiang-wen, JIAN Si-yuan, CHEN Guo-long. Emotional contagion in Twitter [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(1): 71-76.
[9] WANG Xue-bin. Analytical solutions for the multi-term time-space Caputo-Riesz fractional diffusion equations in 2-D and 3-D [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2015, 50(10): 89-94.
[10] WANG Shao-peng, PENG Yan, WANG Jie. Research of the text clustering based on LDA using in network public opinion analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(09): 129-134.
[11] DONG Wei-wei. A new method of DEA efficiency ranking for decision making units with independent subsystems [J]. J4, 2013, 48(1): 89-92.
[12] SHAN Tao1, LIU Zhi-jie1,2*, HU Jia-lei1, TAN Hai-bo1. A reliability grid replica construction research and simulation based on DMRC algorithm [J]. J4, 2012, 47(9): 32-37.
[13] WANG Lei1,2, GUO Si-zong2. Linear formed fractional fuzzy differential equations [J]. J4, 2012, 47(7): 81-84.
[14] ZHAI Ru-kun, JIANG Xiao-yun. The solution of the time-fractional model of heat transport in complex human tissue [J]. J4, 2012, 47(6): 1-4.
[15] LIU Xuan1, XU Jie-ping1*, CHEN Jie2. Music similarity research based on the Web tag [J]. J4, 2012, 47(5): 53-58.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd