Skip to main content
SpringerLink
Log in

CrossRec: Cross-Domain Recommendations Based on Social Big Data and Cognitive Computing

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

With the explosion of social data comes a great challenge called information overloading. To overcome this challenge, recommender systems are expected to support users in quickly accessing the appropriate content. However, cold-start users are a formidable challenge in the design of recommender systems because the conventional recommendation services are based on a single data source, namely, a single field. Considering the advantages of social-based and cross-domain approaches involving further additional data, we propose a cross-domain recommender system, including three approaches, based on multi-source social big data. The proposed approach is expected to effectively alleviate the issues of cold-start users by transferring user preferences from a related auxiliary domain to a target domain. Moreover, the transferred preferences are able to improve the diversity of recommendations. Through adequate evaluations based on an actual dataset in the book and music domains, it is shown that the accuracies of the three proposed approaches are significantly improved compared with the conventional recommender approaches, such as collaborative filtering and matrix factorization. In particular, the proposed approaches are available to provide cold-start users with highly effective recommendations.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Notes

  1. http://www.douban.com

References

  1. Song H, Srinivasan R, Sookoor T, Jeschke S (2017) Smart cities: foundations, principles and applications. Wiley, Hoboken

    Book  Google Scholar 

  2. Baccarelli E, Cordeschi N, Mei A, Panella M, Shojafar M, Stefa J (2016) Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study. IEEE Netw 30(2):54–61

    Article  Google Scholar 

  3. Huang L, Wu J, You F, Lv Z, Song H (2016) Cyclist social force model at unsignalized intersections with heterogeneous traffic. IEEE Trans Indus Inf PP(99):1–1

    Google Scholar 

  4. Congosto M, Basanta-Val P, Sanchez-Fernandez L (2017) T-hoarder: a framework to process twitter data streams. J Netw Comput Appl 83:28–39

    Article  Google Scholar 

  5. Congosto M, Fuentes-Lorenzo D, Sánchez L (2015) Microbloggers as sensors for public transport breakdowns. IEEE Internet Comput 19(6):18–25

    Article  Google Scholar 

  6. Berkovsky S, Freyne J (2015) Web personalization and recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, ser. KDD ’15. ACM, New York, pp 2307–2308. [Online]. Available: http://doi.acm.org/10.1145/2783258.2789995

  7. Chen M, Qian Y, Hao Y, Li Y, Song J (2018) Data-driven computing and caching in 5g networks: architecture and delay analysis. IEEE Wirel Commun 25(1):70–75

    Article  Google Scholar 

  8. Schnabel T, Bennett PN, Dumais ST, Joachims T (2016) Using shortlists to support decision making and improve recommender system performance. In: Proceedings of the 25th international conference on world wide web, ser. WWW ’16. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, pp 987–997. [Online]. Available: https://doi.org/10.1145/2872427.2883012

  9. Loai AT, Mehmood R, Benkhlifa E, Song H (2016) Mobile cloud computing model and big data analysis for healthcare applications. IEEE Access 4:6171–6180

    Article  Google Scholar 

  10. Jiang S, Qian X, Shen J, Fu Y, Mei T (2015) Author topic model-based collaborative filtering for personalized poi recommendations. IEEE Trans Multimed 17(6):907–918

    Google Scholar 

  11. Gu Y, Zhao B, Hardtke D, Sun Y (2016) Learning global term weights for content-based recommender systems. In: Proceedings of the 25th international conference on world wide web, ser. WWW ’16. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, pp 391–400. [Online]. Available: https://doi.org/10.1145/2872427.2883069

  12. Sahoo J, Das AK, Goswami A (2015) An efficient approach for mining association rules from high utility itemsets. Expert Syst Appl 42(13):5754–5778. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S095741741500158X

    Article  Google Scholar 

  13. Sun Y, Song H, Jara AJ, Bie R (2016) Internet of things and big data analytics for smart and connected communities. IEEE Access 4:766–773

    Article  Google Scholar 

  14. Lin C, Wang P, Song H, Zhou Y, Liu Q, Wu G (2016) A differential privacy protection scheme for sensitive big data in body sensor networks. Ann Telecommun 71(9–10):465–475

    Article  Google Scholar 

  15. Narducci F, Musto C, Polignano M, de Gemmis M, Lops P, Semeraro G (2015) A recommender system for connecting patients to the right doctors in the healthnet social network. In: Proceedings of the 24th international conference on world wide web, ser. WWW ’15 Companion. ACM, New York, pp 81–82. [Online]. Available: http://doi.acm.org/10.1145/2740908.2742748

  16. Tinghuai M, Jinjuan Z, Meili T, Yuan T, Abdullah A-D, Mznah A-R, Sungyoung L (2015) Social network and tag sources based augmenting collaborative recommender system. IEICE Trans Inf Syst 98(4):902–910

    Google Scholar 

  17. Chen M, Hao Y, Qiu M, Song J, Wu D, Humar I (2016) Mobility-aware caching and computation offloading in 5g ultra-dense cellular networks. Sensors 16(7):974

    Article  Google Scholar 

  18. Chen M, Hao Y, Hu L, Huang K, Lau VK (2017) Green and mobility-aware caching in 5g networks. IEEE Trans Wirel Commun 16(12):8347–8361

    Article  Google Scholar 

  19. Arnaboldi V, Campana MG, Delmastro F, Pagani E (2016) Pliers: a popularity-based recommender system for content dissemination in online social networks. In: Proceedings of the 31st annual ACM symposium on applied computing, ser. SAC ’16. ACM, New York, pp 671–673. [Online]. Available: http://doi.acm.org/10.1145/2851613.2851940

  20. Sun Z, Han L, Huang W, Wang X, Zeng X, Wang M, Yan H (2015) Recommender systems based on social networks. J Syst Softw 99:109–119

    Article  Google Scholar 

  21. Xu Z, Jiang H, Kong X, Kang J, Wang W, Xia F (2016) Cross-domain item recommendation based on user similarity. Comput Sci Inf Syst 13(2):359–373

    Article  Google Scholar 

  22. Chen M, Zhang Y, Qiu M, Guizani N, Hao Y (2018) Spha: smart personal health advisor based on deep analytics. IEEE Commun Mag 56(3):164–169

    Article  Google Scholar 

  23. Mirbakhsh N, Ling CX (2015) Improving top-n recommendation for cold-start users via cross-domain information. ACM Trans Knowl Discov Data 9(4):33:1–33:19. [Online]. Available: http://doi.acm.org/10.1145/2724720

    Article  Google Scholar 

  24. Kumar V, Shrivastva KMP, Singh S (2016) Cross domain recommendation using semantic similarity and tensor decomposition. Procedia Comput Sci 85:317–324. International conference on computational modelling and security (CMS 2016). [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1877050916305877

    Article  Google Scholar 

  25. Li B, Zhu X, Li R, Zhang C (2015) Rating knowledge sharing in cross-domain collaborative filtering. IEEE Trans Cybern 45(5):1068–1082

    Article  Google Scholar 

  26. Chen M, Herrera F, Hwang K (2018) Cognitive computing: architecture, technologies and intelligent applications. IEEE Access 6:19774–19783

    Article  Google Scholar 

  27. Lee C-S, Wang M-H, Lan S-T (2015) Adaptive personalized diet linguistic recommendation mechanism based on type-2 fuzzy sets and genetic fuzzy markup language. IEEE Trans Fuzzy Syst 23(5):1777–1802

    Article  Google Scholar 

  28. Nilashi M, bin Ibrahim O, Ithnin N, Sarmin NH (2015) A multi-criteria collaborative filtering recommender system for the tourism domain using expectation maximization (em) and pca–anfis. Electron Commer Res Appl 14 (6):542–562

    Article  Google Scholar 

  29. Enrich M, Braunhofer M, Ricci F (2013) Cold-start management with cross-domain collaborative filtering and tags. In: International conference on electronic commerce and web technologies. Springer, pp 101–112

  30. Fernández-Tobías I, Tomeo P, Cantador I, Di Noia T, Di Sciascio E (2016) Accuracy and diversity in cross-domain recommendations for cold-start users with positive-only feedback. In: Proceedings of the 10th ACM conference on recommender systems. ACM, pp 119–122

  31. Cai Y, Leung H-f, Li Q, Min H, Tang J, Li J (2014) Typicality-based collaborative filtering recommendation. IEEE Trans Knowl Data Eng 26(3):766–779

    Article  Google Scholar 

  32. Yang X, Guo Y, Liu Y, Steck H (2014) A survey of collaborative filtering based social recommender systems. Comput Commun 41:1–10

    Article  Google Scholar 

  33. Kannan R, Ishteva M, Park H (2014) Bounded matrix factorization for recommender system. Knowl Inf Syst 39(3):491–511

    Article  Google Scholar 

  34. Luo X, Zhou M, Leung H, Xia Y, Zhu Q, You Z, Li S (2016) An incremental-and-static-combined scheme for matrix-factorization-based collaborative filtering. IEEE Trans Autom Sci Eng 13(1):333–343

    Article  Google Scholar 

  35. Guo G, Zhang J, Thalmann D (2014) Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowl-Based Syst 57:57–68

    Article  Google Scholar 

  36. Gao H, Tang J, Liu H (2015) Addressing the cold-start problem in location recommendation using geo-social correlations. Data Min Knowl Disc 29(2):299–323

    Article  Google Scholar 

  37. Lin J, Sugiyama K, Kan M-Y, Chua T-S (2013) Addressing cold-start in app recommendation: latent user models constructed from twitter followers. In: Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 283–292

  38. Cantador I, Cremonesi P (2014) Tutorial on cross-domain recommender systems. In: Proceedings of the 8th ACM conference on recommender systems. ACM, pp 401–402

  39. Sahebi S, Brusilovsky P (2013) Cross-domain collaborative recommendation in a cold-start context: the impact of user profile size on the quality of recommendation. In: International conference on user modeling, adaptation, and personalization. Springer, pp 289–295

  40. Knowledge C-DT (2015) Social recommendation with cross-domain transferable knowledge. IEEE Trans Knowl Data Eng 27:11

    Google Scholar 

  41. Li B (2011) Cross-domain collaborative filtering: a brief survey. In: 2011 IEEE 23rd International conference on tools with artificial intelligence. IEEE, pp 1085–1086

  42. Hu L, Cao J, Xu G, Cao L, Gu Z, Zhu C (2013) Personalized recommendation via cross-domain triadic factorization. In: Proceedings of the 22nd international conference on world wide web. ACM, pp 595–606

  43. Niwattanakul S, Singthongchai J, Naenudorn E, Wanapu S (2013) Using of Jaccard coefficient for keywords similarity. In: Proceedings of the international multiconference of engineers and computer scientists, vol 1, pp 13–15

  44. Tata S, Patel JM (2007) Estimating the selectivity of tf-idf based cosine similarity predicates. ACM Sigmod Record 36(2):7–12

    Article  Google Scholar 

  45. Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise reduction in speech processing. Springer, pp 1–4

  46. Yang S, Cheema MA, Lin X, Wang W (2015) Reverse k nearest neighbors query processing: experiments and analysis. Proc VLDB Endowt 8(5):605–616

    Article  Google Scholar 

  47. Liu P, Cao J, Liang X, Li W (2015) A two-stage cross-domain recommendation for cold start problem in cyber-physical systems. In: 2015 International conference on machine learning and cybernetics (ICMLC), vol 2. IEEE, 876–882

  48. Jiang M, Cui P, Chen X, Wang F, Zhu W, Yang S (2015) Social recommendation with cross-domain transferable knowledge. IEEE Trans Knowl Data Eng 27(11):3084–3097

    Article  Google Scholar 

  49. Qian S, Zhang T, Hong R, Xu C (2015) Cross-domain collaborative learning in social multimedia. In: Proceedings of the 23rd ACM international conference on multimedia, ser. MM ’15. ACM, New York, pp 99–108. [Online]. Available: http://doi.acm.org/10.1145/2733373.2806234

  50. Sen S, Harper FM, LaPitz A, Riedl J (2007) The quest for quality tags. In: Proceedings of the 2007 international ACM conference on supporting group work. ACM, pp 361–370

  51. Kim J, Han M, Lee Y, Park Y (2016) Futuristic data-driven scenario building: incorporating text mining and fuzzy association rule mining into fuzzy cognitive map. Expert Syst Appl 57:311–323

    Article  Google Scholar 

  52. Zaharia M, Xin RS, Wendell P, Das T, Armbrust M, Dave A, Meng X, Rosen J, Venkataraman S, Franklin MJ et al (2016) Apache spark: a unified engine for big data processing. Commun ACM 59(11):56–65

    Article  Google Scholar 

  53. Basanta-Val P, Audsley NC, Wellings AJ, Gray I, Fernández-García N (2016) Architecting time-critical big-data systems. IEEE Trans Big Data 2(4):310–324

    Article  Google Scholar 

  54. Basanta-Val P, Fernández-García N, Wellings AJ, Audsley NC (2015) Improving the predictability of distributed stream processors. Futur Gen Comput Syst 52:22–36

    Article  Google Scholar 

  55. Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai D, Amde M, Owen S et al (2016) Mllib: machine learning in apache spark. J Mach Learn Res 17(34):1–7

    MathSciNet  MATH  Google Scholar 

  56. Xing EP, Ho Q, Dai W, Kim JK, Wei J, Lee S, Zheng X, Xie P, Kumar A, Yu Y (2015) Petuum: a new platform for distributed machine learning on big data. IEEE Trans Big Data 1(2):49–67

    Article  Google Scholar 

  57. Zollanvari A, Dougherty ER (2014) Moments and root-mean-square error of the Bayesian mmse estimator of classification error in the Gaussian model. Pattern Recogn 47(6):2178–2192

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the China National Natural Science Foundation under Grant 61702553 and the Project of Humanities and Social Sciences (17YJCZH252) funded by the China Ministry of Education (MOE).

Author information

Authors and Affiliations

  1. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China

    Yin Zhang, Xiao Ma & Shaohua Wan

  2. National University of Sciences and Technology, Islamabad, Pakistan

    Haider Abbas

  3. Florida Institute of Technology, Melbourne, FL, USA

    Haider Abbas

  4. Electrical and Computer Engineering Department, University of Idaho, Moscow, ID, 1023, USA

    Mohsen Guizani

Authors
  1. Yin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaohua Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haider Abbas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohsen Guizani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yin Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Ma, X., Wan, S. et al. CrossRec: Cross-Domain Recommendations Based on Social Big Data and Cognitive Computing. Mobile Netw Appl 23, 1610–1623 (2018). https://doi.org/10.1007/s11036-018-1112-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-018-1112-1

Keywords

Search

Navigation