Skip to main content
SpringerLink
Log in

Time-aware user profiling from personal service ecosystem

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The tremendous growth of services available on the Internet makes user profiling an increasing important topic in web personalization. While profiling a user, one of the main challenges is that user preferences and interests usually change over time. It would be much appreciated, if the user profile could describe these changes. Such user profile is defined as time-aware user profile (TUP) in this paper. The TUP can delineate both dynamic user preferences and interests and evolutions of them, which illustrates that it can represent users more clearly and accurately. A TUP consists of a set of time slot-specific user profiles, which is defined as sub-time-aware user profile (sTUP). And a TUP is special for a user; then, its sTUPs separately represent preferences and interests of this user for different time slots. Constructing a TUP needs the constructions of its sTUPs. While constructing a sTUP from the user behavior logs for a time slot, in this paper, we present a novel learning model called personal service ecosystem (PSE) to delineate user preferences and interests naturally. Different from PSE, sTUP is the representation model. For a time slot, the sTUP is constructed based on the PSE recovered from the user behavior logs of this time slot. In terms of a TUP, two main approaches to analyze the evolutions of sTUPs in this TUP are further discussed. They are the evolution analysis of various margins between every two neighboring sTUPs called EA-VM and the evolution analysis of different sTUPs on raw data defined as EA-RD. EA-VM mainly focuses on the fine-grained evolution between two neighboring time slots by analyzing these various margins. It can be further divided into the clustering method and the distribution analysis approach. EA-RD chiefly pays attentions to the overall evolution of such sTUPs. By a comprehensive survey on the raw data of sTUPs, we summarize six interesting overall evolution patterns, including refugee pattern, periodic pattern, stable pattern, fluctuant pattern, emergency pattern and zombie pattern. Eventually, a systematic empirical study based on our collection data has been conducted, where smartphone users are taken as examples to illustrate our proposed models and approaches. And experimental results highlight the superiority of these approaches.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. www.programmableweb.com.

  2. www.myexperiment.org.

  3. www.biocatalogue.org.

References

  1. Han L, Chen G, Li M (2013) A method for the acquisition of ontology-based user profiles. Adv Eng Softw 65:132–137

    Article  Google Scholar 

  2. Xie X, Wang B (2018) Web page recommendation via twofold clustering: considering user behavior and topic relation. Neural Comput Appl 29(1):235–243

    Article  MathSciNet  Google Scholar 

  3. Yao L, Sheng QZ, Ngu AH, Yu J, Segev A (2015) Unified collaborative and content-based web service recommendation. IEEE Trans Serv Comput 8(3):453–466

    Article  Google Scholar 

  4. Wang H, Wang Z, Xu X (2016) Time-aware customer preference sensing and satisfaction prediction in a dynamic service market. In: International conference on service-oriented computing. Springer, Cham, pp 236–251

  5. Hawalah A, Fasli M (2015) Dynamic user profiles for web personalisation. Expert Syst Appl 42(5):2547–2569

    Article  Google Scholar 

  6. Wang Z, Chu D, Xu X (2016) Personal service eco-environment (\(PSE^2\)): a user-centric services computing paradigm. In: International conference on exploring services science. Springer, Cham, pp 141–154

  7. Salonen V, Karjaluoto H (2016) Web personalization: the state of the art and future avenues for research and practice. Telemat Inform 33(4):1088–1104

    Article  Google Scholar 

  8. Pratiwi PS, Xu Y, Li Y, Trost SG, Tjondronegoro DW (2018) User profile ontology to support personalization for e-coaching systems. In: ACM 12th international workshop on data and text mining in biomedical informatics knowledge management in healthcare (DTMBio 2018), 2018-10-22

  9. Zhang Z, Liu Y, Xu G, Chen H (2016) A weighted adaptation method on learning user preference profile. Knowl-Based Syst 112:114–126

    Article  Google Scholar 

  10. de Amo S, Bueno MLP, Alves G, da Silva NFF (2013) Mining user contextual preferences. J Inf Data Manag 4:37–46

    Google Scholar 

  11. Zhang J, Shukla M (2006) Rule-based platform for web user profiling. In: Proceedings of the sixth international conference on data mining (ICDM), pp 1183–1187

  12. Gao M, Liu K, Wu Z (2010) Personalisation in web computing and informatics: theories, techniques, applications, and future research. Inf Syst Front 12(5):607–629

    Article  Google Scholar 

  13. Amoretti M, Belli L, Zanichelli F (2017) UTravel: smart mobility with a novel user profiling and recommendation approach. Pervasive Mobile Comput 38:474–489

    Article  Google Scholar 

  14. Yang YC (2010) Web user behavioral profiling for user identification. Decis Support Syst 49(3):261–271

    Article  Google Scholar 

  15. De Amo S, Diallo MS, Diop CT, Giacometti A, Li D, Soulet A (2015) Contextual preference mining for user profile construction. Inf Syst 49:182–199

    Article  Google Scholar 

  16. Papazoglou MP, Traverso P, Dustdar S, Leymann F (2006) Service-oriented computing research roadmap. Int J Cooper Inf Syst 5:223–255

    Google Scholar 

  17. Barros AP, Dumas M (2006) The rise of web service ecosystems. IT Prof 8:31–37

    Article  Google Scholar 

  18. Yu S, Woodard CJ (2009) Innovation in the programmable web: characterizing the mashup ecosystem. In: Proceedings of international conference service oriented computer, pp 136–147

  19. Weiss M, Gangadharan GR (2010) Modeling the mashup ecosystem: structure and growth. R&D Manag 40:40–49

    Article  Google Scholar 

  20. Wang J, Chen H, Zhang Y (2009) Mining user behavior pattern in mashup community. In: Proceedings of IEEE international conference information reuse and integration, pp 126–131

  21. Huang K, Fan Y, Tan W (2014) Recommendation in an evolving service ecosystem based on network prediction. IEEE Trans Autom Sci Eng 11(3):906–920

    Article  Google Scholar 

  22. Wang X, Feng Z, Chen S, Huang K (2018) DKEM: a distributed knowledge based evolution model for service ecosystem. In: IEEE international conference on web services (ICWS), pp 1–8

  23. Huang K, Yao J, Zhang J, Feng Z (2016) Human-as-a-service: growth in human service ecosystem. IEEE international conference on services computing (SCC), pp 90–97

  24. Rosatti G, Zorzi N, Zugliani D, Piffer S, Rizzi A (2018) A web service ecosystem for high-quality, cost-effective debris-flow hazard assessment. Environ Modell Softw 100:33–47

    Article  Google Scholar 

  25. Yu W, Li S, Tang X, Wang K (2018) An efficient top-k ranking method for service selection based on e-ADMOPSO algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3640-9

    Article  Google Scholar 

  26. Liu X, Li H, Lu X, Xie T, Mei Q, Feng F, Mei H (2018) Understanding diverse usage patterns from large-scale appstore-service profiles. IEEE Trans Softw Eng 44(4):384–411

    Article  Google Scholar 

  27. Petsas T, Papadogiannakis A, Polychronakis M, Markatos EP, Karagiannis T (2013) Rise of the planet of the apps: a systematic study of the mobile app ecosystem. In: Proceedings of the 2013 conference on internet measurement conference, ACM, pp 277–290

  28. Hao Y, Wang Z, Xu X (2016) Global and personal app networks: characterizing social relations among mobile apps. In: International conference on services computing, pp 227–234

Download references

Acknowledgements

Research in this paper is partially supported by the National Key Research and Development Program of China (No 2017YFB1400604), the National Science Foundation of China (61772155, 61802089, 61832014, 61832004, 61472106).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Haifang Wang, Zhiying Tu, Yao Fu, Zhongjie Wang & Xiaofei Xu

Authors
  1. Haifang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiying Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yao Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhongjie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaofei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhiying Tu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest to this work.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Tu, Z., Fu, Y. et al. Time-aware user profiling from personal service ecosystem. Neural Comput & Applic 33, 3597–3619 (2021). https://doi.org/10.1007/s00521-020-05215-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05215-9

Keywords

Search

Navigation