Three-tier IoT-edge-cloud (3T-IEC) architectural paradigm for real-time event recommendation in event-based social networks

Abstract

With the popularization of internet, event-based social networks (EBSNs) have experienced recognition among people for planning and communicating social events. Due to plethora of events occurring over EBSNs and time varying user interests, different recommendation techniques have been employed to make suitable events suggestions to participants. The state of the art event recommendation methods have not explored the importance of real-time data while generating event recommendations. However, we strongly believe that performance of event recommendation in EBSN can significantly be improved if multiple parameters captured through IoT devices may be considered. In this work, a real-time event recommendation problem which involves monitoring user’s current location, present road traffic, and weather conditions is addressed that adopts instant event recommendation. To address this problem, a novel three-tier IoT-edge-cloud based solution for real-time context-aware event recommendation problem named as 3T-IEC has been proposed. The 3T-IEC introduces edge computing layer where IoT data is processed for deriving contextual IoT-based location information along with event recommendation generator in the cloud layer. Further, contextual information such as user’s current location, weather and temporal feasibility has been applied to filter the events. Furthermore, group, category, and economic influences are modeled on filtered events to rank them with help of multiple criteria decision making method. Moreover, personalized weights on influential factors are also learned by using distance method. For practical realization, an android based mobile application-SpotEvent has been developed. Furthermore, the qualitative and quantitative analysis of the 3T-IEC is performed on two real-world datasets acquired from Meetup. The results clearly indicate that recommendation quality of proposed system is better, when compared to its variants and other baseline methods such as VSM (260%), SVD (144%), CAER (335%), Skyline (100%) and SoCast* (16%).

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References

  1. ANSI/ASHRAE (2008) ANSI/ASHRAE Standard 55-2004: thermal environmental conditions for human occupancy. American Society of Heating, Refrigerating and Air-Conditioning Engineers Inc., Atlanta

    Google Scholar 

  2. Adams C (2010) The effects of air temperature on productivity. Ergonomics for the office environment. https://ergonomics.about.com/od/office/a/How-Temperature-Effects-Your-Productivity.htm. Accessed 5 May 2020

  3. Aslam JA, Montague M (2001) Models for metasearch. In: Proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval (SIGIR’01). ACM, New York, NY, USA, pp 276–284

  4. Bader R, Neufeld E, Woerndl W, Prinz V (2011) Context-aware POI recommendations in an automotive scenario using multi-criteria decision making methods. In: Proceedings of the 2011 workshop on context-awareness in retrieval and recommendation (CaRR’11). ACM, New York, NY, USA, pp 23–30

  5. Bakshy E, Eckles D, Yan R, Rosenn I (2012) Social influence in social advertising: evidence from field experiments. In: Proceedings of the 13th ACM conference on electronic commerce (EC’12). ACM, New York, NY, USA, pp 146–161

  6. Boutsis I, Karanikolaou S, Kalogeraki V (2015) Personalized event recommendations using social networks. In: International conference on mobile data management, 15–18 June, Pittsburgh, PA, USA

  7. Campanella G, Ribeiro RA (2011) A framework for dynamic multiple criteria decision making. Decis Support Syst 52(1):52–60

    Article  Google Scholar 

  8. Chen C-C, Tsai J-L (2017) Determinants of behavioral intention to use the personalized location-based mobile tourism application: an empirical study by integrating TAM with ISSM. Future Gener Comput Syst 96:628–638

    Article  Google Scholar 

  9. Chen CC, Wan Y-H, Chung M-C, Sun Y-C (2013) An effective recommendation method for cold start new users using trust and distrust networks. Inf Sci 224:19–36

    MathSciNet  Article  Google Scholar 

  10. Chomicki J, Ciaccia P, Meneghetti N (2013) Skyline queries, front and back. ACM SIGMOD Rec 42(3):6–18

    Article  Google Scholar 

  11. CityPulse (2016) Road traffic data, citypulse: real-time IoT stream processing and large-scale smart city datasets. https://iot.ee.surrey.ac.uk:8080/datasets.html. Accessed 1 May 2020

  12. Daly EM, Geyer W (2011) Effective event discovery: Using location and social information for scoping event recommendations. In: Proceedings of ACM RecSys, pp 277–280

  13. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 3:319–340

    Article  Google Scholar 

  14. De Pessemier T, Minnaert J, Vanhecke K, Dooms S, Martens L (2013) Social recommendations for events. In: Proceedings of CEUR, pp 1–4

  15. Du R, Yu Z, Mei T, Wang Z, Wang Z, Guo B (2014) Predicting activity attendance in event-based social networks: content, context and social influence, In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing (Ubicomp’14). ACM, New York, NY, USA, pp 425–434

  16. Dyer JS, Fishburn PC, Steuer RE, Wallenius J, Zionts S (1992) Multiple criteria decision making, multi attribute theory: the next ten years. Manag Sci 38(5):645–654

    MATH  Article  Google Scholar 

  17. Emrich A, Chapko A, Werth D, Loos P (2014) Adaptive, multi-criteria recommendations for location-based services. In: Proceedings of Hawaii international conference on system sciences, Wailea, Maui, HI, pp 1165–1173

  18. Eventbrite (2020). In: Eventbrite. https://www.eventbrite.com/. Accessed 27 Apr 2020

  19. Fang X, Pan R, Cao G, He X, Dai W (2015) Personalized tag recommendation through nonlinear tensor factorization using Gaussian kernel. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence (AAAI’15). AAAI, Austin, Texas

  20. Galitsky BA (2016) Providing personalized recommendation for attending events based on individual interest profiles. Artif Intell Res 5(1):1–13

    Google Scholar 

  21. Goyal A, Bonchi F, Lakshmanan L (2011) A data-based approach to social influence maximization. Proc VLDB Endowment 5:73–84

    Article  Google Scholar 

  22. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2010) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53

    Article  Google Scholar 

  23. Kayaalp M, Özyer T, Özyer ST (2009) A collaborative and content based event recommendation system integrated with data collection scrapers and services at a social networking site. In: Proceedings of ASONAM, pp 113–118

  24. Lee KCK, Zheng B, Chen C, Chow C-Y (2013) Efficient index based approaches for skyline queries in location-based applications. IEEE Trans Knowl Data Eng 25(11):2507–2520

    Article  Google Scholar 

  25. Lee CC, Lee WC, Cai H, Chi HR, Wu CK, Haase J, Gidlund M (2015) Traffic condition monitoring using weighted kernel density for intelligent transportation. In: Proceedings of the 13th international conference on industrial informatics (INDIN’13). IEEE, Cambridge, UK, pp 624–627

  26. Li X, Cheng X, Su S, Li S, Yang J (2017) A hybrid collaborative filtering model for social influence prediction in event-based social networks. Neurocomputing 230:97–209

    Article  Google Scholar 

  27. Li BYS, Yeung LF, Tsang KF (2014) Analysing traffic condition based on IoT technique. In: Proceedings of the 2014 IEEE international conference on consumer electronics. IEEE, Shenzen, China, pp 1–4

  28. Liao G, Zhao Y, Xie S, Yu PS (2013) An effective latent networks fusion based model for event recommendation in offline ephemeral social networks. In: CIKM, San Francisco, CA, USA, pp 1655–1660

  29. Liu Z, Qu W, Li H (2010) A hybrid collaborative filtering recommendation mechanism for p2p networks. Future Gener Comput Syst 26(8):1409–1417

    Article  Google Scholar 

  30. Liu X, He Q, Tian Y, Lee WC, McPherson J, Han J (2012) Event-based social networks: linking the online and offline social worlds. In: Proceedings of 18th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’12). ACM, Beijing, China, pp 1032–1040

  31. Lyu Y, Chow CY, Wang R, Lee CS (2014) Using multi-criteria decision making for personalized point-of-interest recommendations. In: Proceedings of the 22nd ACM SIGSPATIAL international conference on advances in geographic information systems (SIGSPATIAL ’14). ACM, New York, NY, USA, pp 461–464

  32. Macedo AQ, Marinho LB, Santos RLT (2015) Context-aware event recommendation in event-based social networks. In: ACM conference on recommender systems, 16–20 September, Vienna, Austria

  33. Manning CD, Raghavan P, Schutze H (2008) Introduction to information retrieval. Cambridge University Press, Cambridge

    Google Scholar 

  34. Meetup (2020) We are what we do. In: Meetup. https://www.meetup.com/. Accessed 27 Apr 2020

  35. Minkov E, Charrow B, Ledlie J, Jaakkola T (2010) Collaborative future event recommendation. In: CIKM, Toronto, Ontaria, Canada, pp 819–828

  36. Noguera JM, Barranco MJ, Segura RJ, MartíNez L (2012) A mobile 3D-GIS hybrid recommender system for tourism. Inform Sci 215:37–52

    Article  Google Scholar 

  37. Ogundele TJ, Chow C, Zhang J (2018) SoCaST*: personalized event recommendations for event-based social networks: a multi-criteria decision making approach. IEEE Access 6:27579–27592

    Article  Google Scholar 

  38. Ogundele TJ, Chow CY, Zhang JD (2017) SoCaST: exploiting social, categorical and spatio-temporal preferences for personalized event recommendations. In: Proceedings of ISPAN, pp 38–45

  39. Ogundele TJ, Chow C-Y, Zhang J-D (2017) EventRec: personalized event recommendations for smart event-based social networks. In: Proceedings of IEEE SMARTCOMP, pp 1–8

  40. Openweathermap (2019) Call current weather data for one location. https://openweathermap.org/current. Accessed 27 Apr 2020

  41. Park MH, Park HS, Cho SB (2008) Restaurant recommendation for group of people in mobile environments using probabilistic multi-criteria decision making. In: Lee S, Choo H, Ha S, Shin IC (eds) Computer–human interaction. APCHI 2008. Lecture notes in computer science, vol 5068. Springer, Berlin, Heidelberg, pp 114–122

  42. Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) Adaptive web. Springer, Berlin, Heidelberg, pp 325–341

    Google Scholar 

  43. Pennacchiotti M, Gurumurthy S (2011) Investigating topic models for social media user recommendation. In: Proceedings of the 20th international conference companion on World wide web (WWW’11). ACM, New York, NY, USA, pp 101–102

  44. Pu P, Chen L (2011) A user—centric evaluation framework for recommender systems. In: Proceedings of the 5th conference on recommender systems. ACM, New York, NY, USA, pp 157–164

  45. Qiao Z, Zhang P, Cao Y, Zhou C, Guo L, Fang B (2014) Combining heterogenous social and geographical information for event recommendation. In: Proceedings of the twenty-eighth AAAI conference on artificial intelligence (AAAI’14). AAAI, pp 145–151

  46. Quercia D, Lathia N, Calabrese F, Lorenzo GD, Crowcroft J (2010) Recommending social events from mobile phone location data. In: International conference on data mining, 14–17 December, Sydney, Australia

  47. Ramirez-Garcia X, García-Valdez M (2014). Post-filtering for a restaurant context-aware recommender system. In: Recent advances on hybrid approaches for designing intelligent systems, vol 547. Springer, Cham, pp 695–707

  48. Ricci F, Rokach L, Shapira B, Kantor PB (eds) (2011) Recommender systems handbook. Springer, New York

    Google Scholar 

  49. Saeedi S, Moussa A, El-Sheimy N (2014) Context-aware personal navigation using embedded sensor fusion in smartphones. Sensors 14:5742–5767

    Article  Google Scholar 

  50. Sarwar, B., Karypis, G., Konstan, J., Riedl, J. (2000). Application of dimensionality reduction in recommender systems—a case study. In: ACM WebKDD workshop

  51. Scellato S, Noulas A, Mascolo C (2011) Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’11). ACM, New York, NY, USA, pp 1046–1054

  52. Sohail SS, Siddiqui J, Ali R (2017) Classifications of recommender systems: a review. J Eng Sci Technol Rev 10(4):132–153

    Article  Google Scholar 

  53. Tang J, Hu X, Liu H (2013) Social recommendation: a review. Soc Netw Anal Min 3:1113–1133

    Article  Google Scholar 

  54. Urban data Hack Competition (2017) Traffic flow dataset and parking sensors dataset, smart cities for the citizens. https://urbandatahack.com/datasets.html. Accessed 1 May 2020

  55. Wu W, Zhao J, Zhang C, Meng F, Zhang Z, Zhang Y, Sun Q (2017) Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding. Knowl Based Syst 128:71–77

    Article  Google Scholar 

  56. Xie M, Yin H, Wang H, Xu F, Chen W, Wang S (2016). Learning graph-based POI embedding for location-based recommendation. In: Proceedings of the 25th ACM international on conference on information and knowledge management (CIKM ’16). ACM, New York, NY, USA, pp 15–24

  57. Xu X, Martel J-M, Lamond BF (2001) A multiple criteria ranking procedure based on distance between partial preorders. Eur J Oper Res 133(1):69–80

    MathSciNet  MATH  Article  Google Scholar 

  58. Xu T, Zhong H, Zhu H, Xiong H, Chen E, Liu G (2015) Exploring the impact of dynamic mutual influence on social event participation. In: Proceedings of the 2015 SIAM international conference on data mining (SDM’15). SIAM, pp 262–270

  59. Yang W-S, Cheng H-C, Dia J-B (2008) A location-aware recommender system for mobile shopping environments. Expert Syst Appl 34:437–445

    Article  Google Scholar 

  60. Yin H, Sun Y, Cui B, Hu Z, Chen L (2013) LCARS: a location content-aware recommender system. In: KDD, Chicago, IL, USA, pp 221–229

  61. Yin H, Wang W, Wang H, Chen L, Zhou X (2017) Spatial-aware hierarchical collaborative deep learning for POI recommendation. Proc IEEE Trans Knowl Data Eng 29(11):2537–2551

    Article  Google Scholar 

  62. Yin H, Zou L, Hung N, Huang Z, Zhou X (2018) Joint event-partner recommendation in event-based social networks. In: Proceedings of IEEE 34th international conference on data engineering (ICDE), Paris, pp 929–940

  63. Yu Z, Du R, Guo B, Xu H, Gu T, Wang Z, Zhang D (2015) Who should I invite for my party?: combining user preference and influence maximization for social events. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing (Ubicomp’15). ACM, New York, NY, USA, pp 879–883

  64. Zhang J-D, Chow C-Y (2015) CoRe: exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations. Inf Sci 293:163–181

    Article  Google Scholar 

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Correspondence to Pratibha Mahajan.

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Mahajan, P., Kaur, P.D. Three-tier IoT-edge-cloud (3T-IEC) architectural paradigm for real-time event recommendation in event-based social networks. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02202-9

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Keywords

  • Recommender system (RS)
  • Event based social networks (EBSNs)
  • Real-time event recommendation
  • Internet of things (IoT)
  • Social influence
  • Context-aware
  • Edge computing
  • Multiple-criteria decision making (MCDM)
  • Cold-start problem