Advertisement

Mobile Networks and Applications

, Volume 23, Issue 6, pp 1610–1623 | Cite as

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

  • Yin Zhang
  • Xiao Ma
  • Shaohua Wan
  • Haider Abbas
  • Mohsen Guizani
Article
  • 59 Downloads

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.

Keywords

Cross-domain recommender Social big data Collaborative filtering Association rule 

Notes

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).

References

  1. 1.
    Song H, Srinivasan R, Sookoor T, Jeschke S (2017) Smart cities: foundations, principles and applications. Wiley, HobokenCrossRefGoogle Scholar
  2. 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):54–61CrossRefGoogle Scholar
  3. 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):1–1Google Scholar
  4. 4.
    Congosto M, Basanta-Val P, Sanchez-Fernandez L (2017) T-hoarder: a framework to process twitter data streams. J Netw Comput Appl 83:28–39CrossRefGoogle Scholar
  5. 5.
    Congosto M, Fuentes-Lorenzo D, Sánchez L (2015) Microbloggers as sensors for public transport breakdowns. IEEE Internet Comput 19(6):18–25CrossRefGoogle Scholar
  6. 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. 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):70–75CrossRefGoogle Scholar
  8. 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. 9.
    Loai AT, Mehmood R, Benkhlifa E, Song H (2016) Mobile cloud computing model and big data analysis for healthcare applications. IEEE Access 4:6171–6180CrossRefGoogle Scholar
  10. 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):907–918Google Scholar
  11. 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. 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 CrossRefGoogle Scholar
  13. 13.
    Sun Y, Song H, Jara AJ, Bie R (2016) Internet of things and big data analytics for smart and connected communities. IEEE Access 4:766–773CrossRefGoogle Scholar
  14. 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):465–475CrossRefGoogle Scholar
  15. 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. 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):902–910Google Scholar
  17. 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):974CrossRefGoogle Scholar
  18. 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):8347–8361CrossRefGoogle Scholar
  19. 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. 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 99:109–119CrossRefGoogle Scholar
  21. 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):359–373CrossRefGoogle Scholar
  22. 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):164–169CrossRefGoogle Scholar
  23. 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 CrossRefGoogle Scholar
  24. 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 CrossRefGoogle Scholar
  25. 25.
    Li B, Zhu X, Li R, Zhang C (2015) Rating knowledge sharing in cross-domain collaborative filtering. IEEE Trans Cybern 45(5):1068–1082CrossRefGoogle Scholar
  26. 26.
    Chen M, Herrera F, Hwang K (2018) Cognitive computing: architecture, technologies and intelligent applications. IEEE Access 6:19774–19783CrossRefGoogle Scholar
  27. 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):1777–1802CrossRefGoogle Scholar
  28. 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):542–562CrossRefGoogle Scholar
  29. 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 101–112Google Scholar
  30. 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 119–122Google Scholar
  31. 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):766–779CrossRefGoogle Scholar
  32. 32.
    Yang X, Guo Y, Liu Y, Steck H (2014) A survey of collaborative filtering based social recommender systems. Comput Commun 41:1–10CrossRefGoogle Scholar
  33. 33.
    Kannan R, Ishteva M, Park H (2014) Bounded matrix factorization for recommender system. Knowl Inf Syst 39(3):491–511CrossRefGoogle Scholar
  34. 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):333–343CrossRefGoogle Scholar
  35. 35.
    Guo G, Zhang J, Thalmann D (2014) Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowl-Based Syst 57:57–68CrossRefGoogle Scholar
  36. 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):299–323CrossRefGoogle Scholar
  37. 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 283–292Google Scholar
  38. 38.
    Cantador I, Cremonesi P (2014) Tutorial on cross-domain recommender systems. In: Proceedings of the 8th ACM conference on recommender systems. ACM, pp 401–402Google Scholar
  39. 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 289–295Google Scholar
  40. 40.
    Knowledge C-DT (2015) Social recommendation with cross-domain transferable knowledge. IEEE Trans Knowl Data Eng 27:11Google Scholar
  41. 41.
    Li B (2011) Cross-domain collaborative filtering: a brief survey. In: 2011 IEEE 23rd International conference on tools with artificial intelligence. IEEE, pp 1085–1086Google Scholar
  42. 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 595–606Google Scholar
  43. 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 13–15Google Scholar
  44. 44.
    Tata S, Patel JM (2007) Estimating the selectivity of tf-idf based cosine similarity predicates. ACM Sigmod Record 36(2):7–12CrossRefGoogle Scholar
  45. 45.
    Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise reduction in speech processing. Springer, pp 1–4Google Scholar
  46. 46.
    Yang S, Cheema MA, Lin X, Wang W (2015) Reverse k nearest neighbors query processing: experiments and analysis. Proc VLDB Endowt 8(5):605–616CrossRefGoogle Scholar
  47. 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, 876–882Google Scholar
  48. 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):3084–3097CrossRefGoogle Scholar
  49. 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. 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 361–370Google Scholar
  51. 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 57:311–323CrossRefGoogle Scholar
  52. 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):56–65CrossRefGoogle Scholar
  53. 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):310–324CrossRefGoogle Scholar
  54. 54.
    Basanta-Val P, Fernández-García N, Wellings AJ, Audsley NC (2015) Improving the predictability of distributed stream processors. Futur Gen Comput Syst 52:22–36CrossRefGoogle Scholar
  55. 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):1–7MathSciNetzbMATHGoogle Scholar
  56. 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):49–67CrossRefGoogle Scholar
  57. 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):2178–2192CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information and Safety EngineeringZhongnan University of Economics and LawWuhanChina
  2. 2.National University of Sciences and TechnologyIslamabadPakistan
  3. 3.Florida Institute of TechnologyMelbourneUSA
  4. 4.Electrical and Computer Engineering DepartmentUniversity of IdahoMoscowUSA

Personalised recommendations