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An event recommendation model using ELM in event-based social network

  • Extreme Learning Machine and Deep Learning Networks
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Abstract

In recent years, event-based social network (EBSN) platforms have increasingly entered people’s daily life and become more and more popular. In EBSNs, event recommendation is a typical problem which recommends interested events to users. Different from traditional social networks, both online and off-line factors play an important role in EBSNs. However, the existing methods do not make full use of the online and off-line information, which may lead to a low accuracy, and they are also not efficient enough. In this paper, we propose a novel event recommendation model to solve the above shortcomings. At first, a feature extraction phase is constructed to make full use of the EBSN information, including spatial feature, temporal feature, semantic feature, social feature and historical feature. And then, we transform the recommendation problem to a classification problem and ELM is extended as the classifier in the model. Extensive experiments are conducted on real EBSN datasets. The experimental results demonstrate that our approach is efficient and has a better performance than the existing methods.

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Notes

  1. http://www.meetup.com.

References

  1. Backstrom L, Huttenlocher DP, Kleinberg JM, Lan X (2006) Group formation in large social networks: membership, growth, and evolution. In: KDD, pp 44–54

  2. Cao J, Zhu Z, Shi L, Liu B, Ma Z (2018) Multi-feature based event recommendation in event-based social network. Int J Comput Intell Syst 11(1):618–633

    Article  Google Scholar 

  3. Cheng Y, Yuan Y, Chen L, Giraud-Carrier CG, Wang G (2017) Complex event-participant planning and its incremental variant. In: ICDE, pp 859–870

  4. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  5. Cui L, Shi Y (2014) A method based on one-class SVM for news recommendation. In: ITQM, pp 281–290

  6. De MP, Ferrara E, Rosaci D, Sarne GM (2015) Trust and compactness in social network groups. IEEE Trans Cybern 45(2):205–216

    Article  Google Scholar 

  7. 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: UbiComp, pp 425–434

  8. Feng K, Cong G, Bhowmick SS, Ma S (2014) In search of influential event organizers in online social networks. In: SIGMOD, pp 63–74

  9. Gruber T (2008) Collective knowledge systems: Where the social web meets the semantic web. J Web Sem 6(1):4–13

    Article  Google Scholar 

  10. Han J, Niu J, Chin A, Wang W, Tong C, Wang X (2012) How online social network affects offline events: a case study on douban. In: UIC, pp 752–757

  11. Huang G, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74(1–3):155–163

    Article  Google Scholar 

  12. Huang G, Zhu Q, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  13. Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: IJCNN, pp 985–990

  14. Khrouf H, Troncy R (2013) Hybrid event recommendation using linked data and user diversity. In: RecSys, pp 185–192

  15. Li K, Lu W, Bhagat S, Lakshmanan LVS, Yu C (2014) On social event organization. In: KDD, pp 1206–1215

  16. 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, pp 1655–1660

  17. Liu X, He Q, Tian Y, Lee W, McPherson J, Han J (2012) Event-based social networks: linking the online and offline social worlds. In: KDD, pp 1032–1040

  18. Lu EH, Chen C, Tseng VS (2012) Personalized trip recommendation with multiple constraints by mining user check-in behaviors. In: SIGSPATIAL, pp 209–218

  19. de Macedo AQ, Marinho LB (2014) Event recommendation in event-based social networks. In: Hypertext

  20. de Macedo AQ, Marinho LB, Santos RLT (2015) Context-aware event recommendation in event-based social networks. In: RecSys, pp 123–130

  21. Meo PD, Musial-Gabrys K, Rosaci D, Sarnè GML, Aroyo L (2017) Using centrality measures to predict helpfulness-based reputation in trust networks. ACM Trans Internet Technol 17(1):8:1–8:20

    Google Scholar 

  22. Minkov E, Charrow B, Ledlie J, Teller SJ, Jaakkola TS (2010) Collaborative future event recommendation. In: CIKM, pp 819–828

  23. Naruchitparames J, Gunes MH, Louis SJ (2011) Friend recommendations in social networks using genetic algorithms and network topology. In: CEC, pp 2207–2214

  24. Pessemier TD, Minnaert J, Vanhecke K, Dooms S, Martens L (2013) Social recommendations for events. In: RecSys

  25. Pham MC, Cao Y, Klamma R, Jarke M (2011) A clustering approach for collaborative filtering recommendation using social network analysis. J UCS 17(4):583–604

    Google Scholar 

  26. Pham TN, Li X, Cong G, Zhang Z (2015) A general graph-based model for recommendation in event-based social networks. In: ICDE, pp 567–578

  27. Qiao Z, Zhang P, He J, Cao Y, Zhou C, Guo L (2014) Combining geographical information of users and content of items for accurate rating prediction. In: WWW, pp 361–362

  28. Ren L, Wang W (2018) An svm-based collaborative filtering approach for top-n web services recommendation. Future Gener Comp Syst 78:531–543

    Article  Google Scholar 

  29. Rosaci D (2015) Finding semantic associations in hierarchically structured groups of web data. Formal Asp Comput 27(5–6):867–884

    Article  Google Scholar 

  30. Sánchez LQ, Recio-García JA, Díaz-Agudo B, Jiménez-Díaz G (2013) Social factors in group recommender systems. ACM TIST 4(1):8:1–8:30

    Google Scholar 

  31. She J, Tong Y, Chen L, Song T (2017) Feedback-aware social event-participant arrangement. In: SIGMOD, pp 851–865

  32. Sun J, Feng S, Wang W, Lang C (2013) Personalized image recommendation and retrieval via latent SVM based model. In: ICIMCS, pp 223–226

  33. Tong Y, Meng R, She J (2015) On bottleneck-aware arrangement for event-based social networks. In: ICDE, pp 216–223

  34. Wang Z, He P, Shou L, Chen K, Wu S, Chen G (2015) Toward the new item problem: context-enhanced event recommendation in event-based social networks. In: ECIR, pp 333–338

  35. Wang Z, Sun L, Zhu W, Yang S, Li H, Wu D (2013) Joint social and content recommendation for user-generated videos in online social network. IEEE Trans Multimed 15(3):698–709

    Article  Google Scholar 

  36. Wei H, Hsieh C, Yang L, Estrin D (2016) Grouplink: Group event recommendations using personal digital traces. In: CSCW, pp 110–113

  37. Xia B, Ni Z, Li T, Li Q, Zhou Q (2017) Vrer: context-based venue recommendation using embedded space ranking SVM in location-based social network. Expert Syst Appl 83:18–29

    Article  Google Scholar 

  38. Xu JA, Araki K (2006) A svm-based personal recommendation system for TV programs. In: MMM

  39. Ye M, Yin P, Lee W, Lee DL (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: SIGIR, pp 325–334

  40. Ying JJ, Lu EH, Kuo W, Tseng VS (2012) Urban point-of-interest recommendation by mining user check-in behaviors. In: KDD, pp 63–70

  41. Yuan Q, Cong G, Ma Z, Sun A, Magnenat-Thalmann N (2013) Time-aware point-of-interest recommendation. In: SIGIR, pp 363–372

  42. Zhang W, Wang J, Feng W (2013) Combining latent factor model with location features for event-based group recommendation. In: KDD, pp 910–918

  43. Zhang X, Zhao J, Cao G (2015) Who will attend? - predicting event attendance in event-based social network. In: MDM, pp 74–83

  44. Zhang Y, Wu H, Sorathia VS, Prasanna VK (2013) Event recommendation in social networks with linked data enablement. In: ICEIS, pp 371–379

  45. Zhang Z, Zhao X, Wang G (2017) FE-ELM: a new friend recommendation model with extreme learning machine. Cogn Comput 9(5):659–670

    Article  Google Scholar 

  46. Zhao X, Li X, Liao L, Song D, Cheung WK (2015) Crafting a time-aware point-of-interest recommendation via pairwise interaction tensor factorization. In: KSEM, pp 458–470

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Acknowledgements

The work is supported by the National Key R&D Program of China (Grant No.2016YFC1401900), the National Natural Science Foundation of China (Grant Nos. U1811262, 61332006, 61332014, 61328202, U1401256, 61572119, 61622202, 61572121, 61702086, 61672145 and 61732003), the Fundamental Research Funds for the Central Universities (Grant Nos. N150402005, N171604007 and N171904007), the Natural Science Foundation of Liaoning Province (Grant No. 20170520164) and the China Postdoctoral Science Foundation (Grant Nos. 2018M631358 and 2018M631806). Yurong Cheng is the corresponding author.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Boyang Li & Yongjiao Sun

  2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Guoren Wang & Yurong Cheng

  3. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China

    Xin Bi

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  1. Boyang Li
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  2. Guoren Wang
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Correspondence to Guoren Wang.

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Li, B., Wang, G., Cheng, Y. et al. An event recommendation model using ELM in event-based social network. Neural Comput & Applic 32, 14375–14384 (2020). https://doi.org/10.1007/s00521-019-04344-0

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