Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

The media-oriented cross domain recommendation method

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

With the rapid development of modern high-tech, such as big data and artificial intelligence, the demand for cross-media services is also greatly improved. Since different modes of cross-media data apply different dimensions and different attributes of the underlying features to present data, many tasks need to work collaboratively to handle multiform of information (including text, audio, video, image, etc.), so as to build cross-media analysis and reasoning. Through the cross-media, the method could express the same semantic information from their own side and could reflect the specific information more fully than a single media object and its specific modal. The same information is cross spread and integrated across different kinds of media objects. Only conducting fusion analysis to these multi-modal media, can ones fully and correctly understand the content information contained in the cross-media complex, which also adds difficulty in cross-media information recommendation process. At the same time, data sparsity, cold start and scalability issues of traditional recommendation system have long been unsolved, and as such it cannot adapt to the personalized service needs in cross-media applications. Focusing on the field of media information recommendation and taking the media data, user behavior data and project attribute information as the information source, this paper aims at researching the cross-domain recommendation algorithm. With the help of the label data, matrix decomposition, the author constructs a media-oriented cross-domain recommendation model in order to improve the recommendation accuracy and solve the data sparsity, cold start problems of media information recommendation technology, exploring a high-accurate media-oriented cross-domain recommendation method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Adomavicius G, Zhang J (2015) Improving stability of recommender systems: a meta-algorithmic approach. IEEE Trans Knowl Data Eng 27(6):1573–1587

    Article  Google Scholar 

  2. Anand D, Bharadwaj KK (2011) Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities[J]. Expert Syst Appl 38(5):5101–5109

    Article  Google Scholar 

  3. Bach NX, Hai ND, Tu MP (2016) Personalized recommendation of stories for commenting in forum-based social media. Inf Sci 352-353(C):48–60

    Article  Google Scholar 

  4. Bobadilla J, Ortega F, Hernando A et al (2011) Improving collaborative filtering recommender system results and performance using genetic algorithms[J]. Knowl-Based Syst 24(8):1310–1316

    Article  Google Scholar 

  5. Chen C, Li K, Ouyang A, et al. (2018) GFlink: An In-Memory Computing Architecture on Heterogeneous CPU-GPU Clusters for Big Data. IEEE Trans Paral Distrib Syst (99):1

  6. Cheng-Hui HUANG, Jian YIN, Fang HOU (2011) A text similarity measurement combining word semantic information with TF-IDF method. Chin J Comput 34(5):856–864

    Article  Google Scholar 

  7. Chun-Hua YU, Liu XJ, Bin LI et al (2017) Mobile service recommendation based on context similarity and social network[J]. Acta Electron Sin 45(6):1530–1536

    Google Scholar 

  8. Cook D, Feuz KD, Krishnan NC (2013) Transfer learning for activity recognition: a survey[J]. Knowl Inf Syst 36(3):537–556

    Article  Google Scholar 

  9. Cremonesi P, Tripodi A, Turrin R (2011) Cross-domain recommender systems,” in Proc. IEEE 11th Int. Conf. Data Mining Workshops (ICDMW), Vancouver, 496–503

  10. Dou Y, Yang H, Deng X (2017) A survey of collaborative filtering algorithms for social recommender systems, international conference on semantics, knowledge and grids. IEEE:40–46

  11. Enrich M, Braunhofer M, Ricci F (2013) Cold-Start Management with Cross-Domain Collaborative Filtering and Tags [J] 152:101–112

  12. Fan LY, Zuo WL, Wang Y et al (2017) Research on recommender system model based on differential privacy and time series[J]. Acta Electron Sin 45(9):2057–2064

    Google Scholar 

  13. Fang YN, Guo YF, Ding XT et al (2013) An improved regularized singular value decomposition recommender algorithm based on tag transfer learning[J]. J Electron Inf Technol 35(12):3046–3050

    Article  Google Scholar 

  14. Fernándeztobías I, Cantador I (2014) Personality-aware collaborative filtering: an empirical study in multiple domains with Facebook data[J]. Lecture Note Bus Inform Proc 188:125–137

    Article  Google Scholar 

  15. Fernandez-Tobias, Cantador I (2014) Exploiting social tags in matrix factorization models for cross-domain collaborative filtering[C]. Proceedings of the First Workshop on New Trends in Content-based Recommender Systems, Foster City, pp 34–41

    Google Scholar 

  16. Gao QL, Ling G, Yang JF et al (2015) A preference elicitation method based on Users' cognitive behavior for context-aware recommender system[J]. Chin J Comput 9:1767–1776

    MathSciNet  Google Scholar 

  17. Guo G (2013) Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems[C]// ACM conference on recommender systems. ACM:451–454

  18. Han T, Yao H, Xu C et al (2017) Dancelets Mining for Video Recommendation Based on dance styles[J]. IEEE Trans Multimed 19(4):712–724

    Article  Google Scholar 

  19. Hsieh CJ, Dhillon IS (2011) Fast coordinate descent methods with variable selection for non-negative matrix factorization[C]. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, pp 1064–1072

    Google Scholar 

  20. Hu Y, Peng Q, Hu X et al (2015) Time aware and data sparsity tolerant web service recommendation based on improved collaborative filtering[J]. IEEE Trans Serv Comput 8(5):782–794

    Article  Google Scholar 

  21. Huang Z, Zeng D, Chen H (2015) A comparison of collaborative-filtering algorithms for ecommerce. IEEE Intell Syst 22(5):68–78

    Article  Google Scholar 

  22. Hui-Si OU, Cao J (2016) Survey on research and Progress of cross-domain recommendation[J]. J Chin Comput Syst 37(7):1411–1416

    Google Scholar 

  23. Jiang M, Cui P, Chen X et al (2015) Social recommendation with cross-domain transferable knowledge[J]. IEEE Trans Knowl Data Eng 27(11):3084–3097

    Article  Google Scholar 

  24. Jie-Yue HE, Bei MA (2016) Based on real-valued conditional restricted Boltzmann machine and social network for collaborative filtering[J]. Chin J Comput 1:183–195

    Google Scholar 

  25. Kim S, Kim H, Min JK (2018) An efficient parallel similarity matrix construction on MapReduce for collaborative filtering. J Supercomput 6:1–19

    Google Scholar 

  26. Kong XX, Su BC, Wang HZ, Gao H, Li JZ (2017) Research on the modeling and related algorithms of label-weight rating based recommendation system. Chin J Comput 40(6):1440–1452

    Google Scholar 

  27. Koren Y (2009) Collaborative filtering with temporal dynamics[J]. Commun ACM 53(4):89–97

    Article  Google Scholar 

  28. Lei X, Qian X, Zhao G (Sep. 2016) Rating prediction based on social sentiment from textual reviews. IEEE Trans Multimed 18(9):1910–1921

    Article  Google Scholar 

  29. Li B (2011) Cross-domain collaborative filtering: a brief survey[C]. IEEE Int Conf TOOLS Artificial Intel IEEE:1085–1086

  30. Li J, Zhang W (2014) Conditional restricted Boltzmann Machines for Cold Start Recommendations[J]. Comput Therm Sci

  31. Li B, Yang Q, Xue X (2009) Can Movies and Books Collaborate Cross-Domain Collaborative Filtering for Sparsity Reduction[C]. IJCAI 2009, Proceedings of the, International Joint Conference on Artificial Intelligence, Pasadena, California, Usa, July. DBLP, 2009:2052–2057

  32. Li B, Yang Q, Xue X (2009) Transfer learning for collaborative filtering via a rating-matrix generative model[C]. International Conference on Machine Learning, ICML 2009, Montreal, pp 617–624

    Google Scholar 

  33. Li B, Zhu X, Li R et al (2015) Rating knowledge sharing in cross-domain collaborative filtering[J]. IEEE Trans Cybernet 45(5):1054

    Article  Google Scholar 

  34. Li L, Peng W, Kataria S et al (2015) Recommending users and communities in social media. ACM Trans Knowl Discov Data 10(2):1–27

    Article  Google Scholar 

  35. Li K, Yang W, Li K (2015) Performance analysis and optimization for SpMV on GPU using probabilistic modeling. IEEE Trans Paral Distribut Syst 26(1):196–205

    Article  MathSciNet  Google Scholar 

  36. Liu NN, Meng X, Liu C et al (2011) Wisdom of the better few: cold start recommendation via representative based rating elicitation[C]// ACM conference on recommender systems. ACM:37–44

  37. Liu J, Tang M, Zheng Z et al (2016) Location-aware and personalized collaborative filtering for web service recommendation[J]. IEEE Trans Serv Comput 9(5):686–699

    Article  Google Scholar 

  38. Liu MJ, Wang W, Yang-Xi LI et al (2017) AttentionRank+: a graph-based recommendation combining attention relationship and multi-behaviors[J]. Chin J Comput 40(3):634–648

    Google Scholar 

  39. Pan W, Liu NN, Xiang EW, Yang Q (2011) “Transfer learning to predict missing ratings via heterogeneous user feedbacks,” in Proc. 22nd Int. Joint Conf. Artif. Intell. (IJCAI). 2318–2323

  40. Rai S, Rai S, Rai S (2017) Measures of similarity in memory-based collaborative filtering recommender system: a comparison, multidisciplinary international social networks conference. ACM 32

  41. Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo[C]. Int Conf Mach Learn ACM:880–887

  42. Sarwar B, Karypis G, Konstan J et al (2001) Item-based collaborative filtering recommendation algorithms[C]. international conference on world wide web. ACM:285–295

  43. Singh A P, Kumar G, Gupta R (2008) Relational learning via collective matrix factorization[C]. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, :650–658

  44. Sun GF, Le WU, Liu Q et al (2013) Recommendations based on collaborative filtering by exploiting sequential behaviors[J]. J Software 24(11):2721–2733

    Article  Google Scholar 

  45. Sun L, Wang X, Wang Z et al (2017) Social-aware video recommendation for online social groups[J]. IEEE Trans Multimed 19(3):609–618

    Article  Google Scholar 

  46. Tan S, Bu J, Qin X et al (2014) Cross domain recommendation based on multi-type media fusion. Neurocomputing 127(3):124–134

    Article  Google Scholar 

  47. Wang J, Li SJ, Yang S, Jin H, Yu W (2017) A new transfer learning model for cross-domain recommendation. Chin J Comput 40:2367–2380

    MathSciNet  Google Scholar 

  48. Wu S, Guo W, Xu S et al (2017) Coupled topic model for collaborative filtering with user-generated content[J]. IEEE Trans Human-Mach Syst 46(6):908–920

    Article  Google Scholar 

  49. Wu Q, Huang M, Mu Y (2018) A collaborative filtering algorithm based on user similarity and trust, web information systems and applications conference. IEEE:263–266

  50. Wu X, Huang Y, Wang S (2018) A new similarity computation method in collaborative filtering based recommendation system, vehicular technology conference. IEEE:1–5

  51. Xiong L, Chen X, Huang T K, et al. (2010) Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization[C]. Siam International Conference on Data Mining, SDM 2010, April 29–May 1, 2010, Columbus, Ohio, Usa. DBLP, 211–222

  52. Yin GS, Zhang YN, Dong YX et al (2014) A constrained trust recommendation using probabilistic matrix factorization[J]. Acta Electron Sin 42(5):904–911

    Google Scholar 

  53. Yuan T, Cheng J, Zhang X et al (2016) Enriching one-class collaborative filtering with content information from social media. Multimedia Systems 22(1):51–62

    Article  Google Scholar 

  54. Zhang Y, Cao B, Yeung D-Y (2010) “Multi-domain collaborative filtering,” in Proc. 26th Conf. Uncertainty Artif. Intell. (UAI), pp. 725–732

  55. Zhang L, Bai LS, Zhou T (2013) Crossing recommendation based on multi-B2C behavior[J]. J Univ Electron Sci Technol Chin 42(1):154–160

    Google Scholar 

  56. Zhang L, Zhao Y, Zhu ZF (2017) Advances in semantically shared subspace learning for cross-media data. Chin J Comput 40(6):1394–1421

    Google Scholar 

  57. Zhao WX et al (2016) Connecting social media to e-commerce: cold-start product recommendation using microblogging information. IEEE TransKnowl Data Eng 28(5):1147–1159

    Article  Google Scholar 

  58. Zhao Z, Lu H, Cai D et al (2016) User preference learning for online social recommendation[J]. IEEE Trans Knowl Data Eng 28(9):2522–2534

    Article  Google Scholar 

  59. Zhong E, Fan W, Wang J, Xiao L, Li Y (2012) “ComSoc: Adaptive transfer of user behaviors over composite social network,” in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining (KDD), 696–704

  60. Zhu KG, Yang D, Cui Q, Hao CL (2016) Cross recommendation model in solving cold start problem. Comput Appl Software 33(5):66–72

    Google Scholar 

Download references

Acknowledgements

The work is supported by grants from the Fundamental Research Funds for the Central Universities and University Research Program of Communication University of China (3132018XNG1841). We thank the reviewers and editor for their helpful comments.

Author information

Authors and Affiliations

  1. Communication University of China, Beijing, China

    Chen Li & Xinrui Yuan

Authors
  1. Chen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinrui Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chen Li.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported by Fundamental Research Funds for the Central Universities and University Research Program of Communication University of China (3132018XNG1841).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, C., Yuan, X. The media-oriented cross domain recommendation method. Multimed Tools Appl 78, 28757–28777 (2019). https://doi.org/10.1007/s11042-018-6720-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6720-z

Keywords

Search

Navigation