Skip to main content
SpringerLink
Log in

Application of Business Big Data Management and Decision Making

  • Chapter
  • First Online:
E-Commerce Big Data Mining and Analytics

Part of the book series: Advanced Studies in E-Commerce ((ASEC))

  • 364 Accesses

Abstract

The high socialization and ubiquity of emerging e-commerce brings together huge user groups and potential business opportunities. A large number of malicious users gain economic benefits by generating and spreading false opinions and junk information. The analysis and detection of malicious user behavior has become a hot field in the interdisciplinary field of e-commerce.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 69.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chen G, Wang J, Guo X, Xu X, Hu L, Liao X (2013) Behavior of emerging E-commerce participants. Tsinghua University Press

    Google Scholar 

  2. Mo Q, Yang K (2014) Research on network spammers identification. J Softw 25(7):1505–1526

    Google Scholar 

  3. Zhang F, Xu S (2008) Survey on security issues and technology of recommender systems. Appl Res Comput 25(3):656–659

    Google Scholar 

  4. Lee K, Caverlee J, Webb S (2010) The social honeypot project: protecting online communities from spammers. In: Proceedings of the 19th international conference on world wide web (WWW 2010). ACM, pp 1139–1140

    Google Scholar 

  5. Hu X, Tang J, Liu H (2014) Online social spammer detection. In: Twenty-eighth AAAI conference on artificial intelligence (AAAI 2014). AAAI Press, pp 59–65

    Google Scholar 

  6. Hu X, Tang J, Zhang Y et al (2013) Social spammer detection in microblogging. In: Proceedings of the twenty-third international joint conference on artificial intelligence (IJCAI 2013). AAAI Press, pp 2633–2639

    Google Scholar 

  7. Benevenuto F, Magno G, Rodrigues T et al (2010) Detecting spammers on twitter. In: Collaboration, electronic messaging, anti-abuse and spam conference (CEAS 2010), pp 6–12

    Google Scholar 

  8. Lecun Y, Boser B, Denker J, Henderson D (2014) Backpropagation applied to handwritten zip code recognition. Neural Comput 1:541–551

    Article  Google Scholar 

  9. Mukherjee A, Venkataraman V, Liu B (2013) What Yelp fake review filter might be doing. In: Proceedings of the 7th international AAAI conference on weblogs and social media (ICWSM 2013), Cambridge, MA, pp 409–418

    Google Scholar 

  10. Xie S, Wang G, Lin S et al (2012) Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2012), Beijing, pp 823–831

    Google Scholar 

  11. Jindal N, Liu B (2008) Opinion spam and analysis. In: Proceedings of the first ACM international conference on web search and data mining (WSDM 2008), Stanford, pp 219–230

    Google Scholar 

  12. Jindal N, Liu B, Lim EP (2010) Finding unusual review patterns using unexpected rules. In: Proceedings of the 19th ACM conference on information and knowledge management (CIKM 2010). ACM, pp 1549–1552

    Google Scholar 

  13. Shojaee S, Murad MAA, Bin Azman A et al (2013) Detecting deceptive reviews using lexical and syntactic features. In: Proceedings of the 13th IEEE international conference on intelligent systems design and applications (ISDA 2013), Selangor, pp 53–58

    Google Scholar 

  14. Mccord M, Chuah M (2011) Spam detection on twitter using traditional classifiers. In: Autonomic and trusted computing. Springer Berlin Heidelberg, pp 175–186

    Google Scholar 

  15. Akoglu L, McGlohon M, Faloutsos C (2010) Oddball: spotting anomalies in weighted graphs. In: 14th Pacific-Asia conference on advances in knowledge discovery and data mining. Springer Berlin Heidelberg, pp 410–421

    Google Scholar 

  16. Muller E, Sánchez PI, Mulle Y et al (2013) Ranking outlier nodes in subspaces of attributed graphs. In: 2013 IEEE 29th international conference on data engineering workshops (ICDEW). IEEE, pp 216–222

    Google Scholar 

  17. Li N, Sun H, Chipman KC et al (2014) A probabilistic approach to uncovering attributed graph anomalies. In: Proceedings of the 2014 SIAM international conference on data mining (SDM 2014). SIAM, pp 82–90

    Google Scholar 

  18. Gao J, Liang F, Fan W et al (2010) On community outliers and their efficient detection in information networks. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 813–822

    Google Scholar 

  19. Jiang M, Cui P, Beutel A et al (2014) Inferring strange behavior from connectivity pattern in social networks. In: 18th Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD 2014). Springer International Publishing, pp 126–138

    Google Scholar 

  20. Prakash BA, Sridharan A, Seshadri M et al (2010) EigenSpokes: surprising patterns and scalable community chipping in large graphs. In: 14th Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD 2010). Springer Berlin Heidelberg, pp 435–448

    Google Scholar 

  21. Shah N, Beutel A, Gallagher B et al (2014) Spotting suspicious link behavior with fBox: an adversarial perspective. In: 2014 IEEE international conference on data mining (ICDM 2014). IEEE, pp 959–964

    Google Scholar 

  22. Cheng H, Tan PN, Potter C et al (2009) Detection and characterization of anomalies in multivariate time series. In: Proceedings of the SIAM international conference on data mining (SDM 2009). SIAM, pp 413–424

    Google Scholar 

  23. Li X, Han J (2007) Mining approximate top-k subspace anomalies in multi-dimensional time-series data. In: Proceedings of the 33rd international conference on very large data bases (VLDB 2007). VLDB Endowment, pp 447–458

    Google Scholar 

  24. Izakian H, Pedrycz W (2014) Anomaly detection and characterization in spatial time series data: a cluster-centric approach. IEEE Trans Fuzzy Syst 22(6):1612–1624

    Article  Google Scholar 

  25. Beutel A, Xu W, Guruswami V et al (2013) CopyCatch: stopping group attacks by spotting lockstep behavior in social networks. In: Proceedings of the 22nd international conference on world wide web (WWW 2013). ACM, pp 119–130

    Google Scholar 

  26. Ferraz Costa A, Yamaguchi Y, Juci Machado Traina A et al (2015) RSC: mining and modeling temporal activity in social media. In: Proceedings of the 21st ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2015). ACM, pp 269–278

    Google Scholar 

  27. Lim EP, Nguyen VA, Jindal N et al (2010) Detecting product review spammers using rating behaviors. In: Proceedings of the 19th ACM international conference on information and knowledge management (CIKM 2010). ACM, pp 939–948

    Google Scholar 

  28. Mukherjee A, Kumar A, Liu B et al (2013) Spotting opinion spammers using behavioral footprints. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2013). ACM, pp 632–640

    Google Scholar 

  29. Lin C, He J, Zhou Y et al (2013) Analysis and identification of spamming behaviors in sina weibo microblog. In: Proceedings of the 7th workshop on social network mining and analysis (SNAKDD 2013). ACM, Article No. 5

    Google Scholar 

  30. Lam SK, Riedl J (2004) Shilling recommender systems for fun and profit. In: Proceedings of the 13th international conference on world wide web (WWW 2004). ACM, New York, pp 393–402

    Google Scholar 

  31. Günnemann S, Günnemann N, Faloutsos C (2014) Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2014). ACM, pp 841–850

    Google Scholar 

  32. Zhou W, Koh YS, Wen J et al (2014) Detection of abnormal profiles on group attacks in recommender systems. In: Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval (SIGIR 2014). ACM, pp 955–958

    Google Scholar 

  33. Zhang Y, Tan Y, Zhang M et al (2015) Catch the black sheep: unified framework for shilling attack detection based on fraudulent action propagation. In: Proceedings of the 24th international conference on artificial intelligence (IJCAI 2015). AAAI Press, pp 2408–2414

    Google Scholar 

  34. Gunes I, Kaleli C, Bilge A et al (2014) Shilling attacks against recommender systems: a comprehensive survey. Artif Intell Rev 42(4):767–799

    Article  Google Scholar 

  35. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2008). ACM, pp 426–434

    Google Scholar 

  36. Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th international conference on machine learning (ICML 2008). ACM, pp 880–887

    Google Scholar 

  37. Koren Y (2010) Collaborative filtering with temporal dynamics. Commun ACM 53(4):89–97

    Article  Google Scholar 

  38. Beutel A, Murray K, Faloutsos C et al (2014) CoBaFi: collaborative Bayesian filtering. In: Proceedings of the 23rd international conference on world wide web (WWW 2014). ACM, pp 97–108

    Google Scholar 

  39. Chirita PA, Nejdl W, Zamfir C (2005) Preventing shilling attacks in online recommender systems. In: Proceedings of the 7th annual ACM international workshop on web information and data management (WIDM 2005), Bremen, pp 67–74

    Google Scholar 

  40. Williams CA, Mobasher B (2006) Profile injection attack detection for securing collaborative recommender systems. Technical report, DePaul University

    Google Scholar 

  41. Burke R, Mobasher B, Williams C et al (2006) Classification features for attack detection in collaborative recommender systems. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2006). ACM, pp 542–547

    Google Scholar 

  42. Bank Card Professional Committee of China Banking Association (2018) Blue book on the development of China’s bank card industry 2018. China Finance Press, Beijing

    Google Scholar 

  43. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  MATH  Google Scholar 

  44. Dong S, Huang Z (2013) Analysis of random forest theory. Ensemble Technol 2(1):1–7

    Google Scholar 

  45. Wang Y, Xia S (2018) Integrated study of a random forest algorithm review. J Inf Commun Technol 12(1):49–55

    Google Scholar 

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

    Article  MATH  Google Scholar 

  47. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119–139

    Article  MathSciNet  MATH  Google Scholar 

  48. Lecun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  49. Lecun Y, Boser B, Denker J et al (2014) Backpropagation applied to handwritten zip code recognition. Neural Comput 1:541–551

    Article  Google Scholar 

  50. Hsu TC, Liou S, Wang Y et al (2019) Enhanced recurrent neural network for combining static and dynamic features for credit card default prediction. In: ICASSP 2019–2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), Hof, 12–17 May 2019, pp 1572–1576

    Google Scholar 

  51. Liu M, Zhang S, He Y (2017) Credit card customer default prediction based on improved fuzzy neural network. Fuzzy Syst Math 31(1):143–148

    Google Scholar 

  52. Wan M, Wang D, Goldman M et al (2017) Modeling consumer preferences and price sensitivities from large-scale grocery shopping transaction logs. In: Proceedings of the 26th international conference on world wide web, pp 1103–1112

    Google Scholar 

  53. Zhang J, Wedel M (2009) The effectiveness of customized promotions in online and offline stores. J Mark Res 46(2):190–206

    Article  Google Scholar 

  54. Cheng AJ, Chen YY, Huang YT et al (2011) Personalized travel recommendation by mining people attributes from community-contributed photos. In: Proceedings of the 19th ACM international conference on multimedia, pp 83–92

    Google Scholar 

  55. Khan MUS, Khalid O, Huang Y et al (2015) MacroServ: a route recommendation service for large-scale evacuations. IEEE Trans Serv Comput 10(4):589–602

    Article  Google Scholar 

  56. Wen YT, Yeo J, Peng WC et al (2017) Efficient keyword-aware representative travel route recommendation. IEEE Trans Knowl Data Eng 29(8):1639–1652

    Article  Google Scholar 

  57. Liu Q, Chen E, Xiong H et al (2014) A cocktail approach for travel package recommendation. IEEE Trans Knowl Data Eng 26(2):278–293

    Article  Google Scholar 

  58. Ge Y, Liu Q, Xiong H et al (2011) Cost-aware travel tour recommendation. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 983–991

    Google Scholar 

  59. Ge Y, Xiong H, Tuzhilin A et al (2011) Collaborative filtering with collective training. In: Proceedings of the 5th ACM conference on recommender systems, pp 281–284

    Google Scholar 

  60. Pazzani MJ, Billsus D (2007) Content-based recommendation systems. Springer, Berlin, pp 325–341

    Google Scholar 

  61. Agarwal D, Chen BC (2009) Regression-based latent factor models. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 19–28

    Google Scholar 

  62. Koohi H, Kiani K (2016) User based collaborative filtering using fuzzy C-means. Measurement 91:134–139

    Article  Google Scholar 

  63. Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80

    Google Scholar 

  64. Ge Y, Xiong H, Tuzhilin A et al (2014) Cost-aware collaborative filtering for travel tour recommendations. ACM Trans Inf Syst 32(1):Article 4

    Google Scholar 

  65. Gantner Z, Drumond L, Freudenthaler C et al (2010) Learning attribute-to-feature mappings for cold-start recommendations. In: Proceedings of the 10th IEEE international conference on data mining, pp 176–185

    Google Scholar 

  66. Ahn HJ (2008) A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf Sci 178(1):37–51

    Article  Google Scholar 

  67. Burke R (2007) Hybrid web recommender systems. Springer, Berlin, pp 377–408

    Google Scholar 

  68. Koren Y (2009) Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 447–456

    Google Scholar 

  69. Ma H, King I, Lyu MR (2011) Learning to recommend with explicit and implicit social relations. ACM Trans Intell Syst Technol 2(3):Article 29

    Google Scholar 

  70. Wang J, Zhang Y (2011) Utilizing marginal net utility for recommendation in ecommerce. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, pp 1003–1012

    Google Scholar 

  71. Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on machine learning, pp 791–798

    Google Scholar 

  72. Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21st ACM SIGKDD international conference on knowledge discovery and data mining, pp 1235–1244

    Google Scholar 

  73. He X, Liao L, Zhang H et al (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173–182

    Google Scholar 

  74. He X, He Z, Song J et al (2018) NAIS: neural attentive item similarity model for recommendation. IEEE Trans Knowl Data Eng 30(12):2354–2366

    Article  Google Scholar 

  75. Chen J, Zhang H, He X et al (2017) Attentive collaborative filtering: multimedia recommendation with item- and component-level attention. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 335–344

    Google Scholar 

  76. Devooght R, Bersini H (2017) Long and short-term recommendations with recurrent neural networks. In: Proceedings of the 25th conference on user modeling, adaptation and personalization, pp 13–21

    Google Scholar 

  77. Sedhain S, Menon AK, Sanner S et al (2015) AutoRec: autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on world wide web, pp 111–112

    Google Scholar 

  78. Xue HJ, Dai X, Zhang J et al (2017) Deep matrix factorization models for recommender systems. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 3203–3209

    Google Scholar 

  79. Covington P, Adams J, Sargin E (2016) Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM conference on recommender systems, pp 191–198

    Google Scholar 

  80. Guo H, Tang R, Ye Y et al (2017) DeepFM: a factorization-machine based neural network for CTR prediction. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 1725–1731

    Google Scholar 

  81. Elkahky AM, Song Y, He X (2015) A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th international conference on world wide web, pp 278–288

    Google Scholar 

  82. He W, Xu F (2013) Promotional goods inventory model with demand dependent on inventory and shortage partially delayed. J Comput Appl 33(10):2950–2959

    Google Scholar 

  83. Huang Y, Zhang Y (2021) Sales forecasting of E-commerce industry based on GM(1, N)-Prophet combination model. J Southwest Univ Natl (Nat Sci Ed) 47(3):317–325

    Google Scholar 

  84. Luo Y, Lv Y, Li B (2009) Hybrid cigarette sales forecasting model based on ARMA. Appl Res Comput 26(7):2664–2668

    Google Scholar 

  85. Chang B, Zhang H, Liao C et al (2018) Retail sales forecasting based on selective ensemble ARMA combination model. Comput Meas Control 26(5):132–135

    Google Scholar 

  86. Feng C, Chen Z (2019) Application of weighted combination model based on XGBoost and LSTM in sales forecasting. Appl Comput Syst 28(10):226–232

    Google Scholar 

  87. Ma C, Wang X (2018) Dish sales prediction based on LSTM network model. Mod Comput (Prof Ed) 23:26–30

    Google Scholar 

  88. Li Z, Wu Q (2019) Research on stock prediction algorithm based on LSTM neural network. Fujian Comput 35(7):41–43

    Google Scholar 

  89. Li Z, Zhang K (2020) Comparison research of car sales forecasting model based on BP algorithm and LSTM algorithm. Econ Res Guide 20:84–88+93

    Google Scholar 

  90. Wang Y (2018) Research on E-commerce demand forecasting based on LSTM neural network. Master’s thesis, Shandong University, Jinan

    Google Scholar 

  91. Jiang W (2019) Research on commodity sales forecasting based on WaveNet-LSTM network. Master’s thesis, Guangdong University of Technology, Guangzhou

    Google Scholar 

  92. Law M, Kwok R, Ng M (2016) An extended online purchase intention model for middle-aged online users. Electron Commer Res Appl 20:132–146

    Article  Google Scholar 

  93. Li D, Zhao G, Wang Z et al (2015) A method of purchase prediction based on user behavior log. In: Proceedings of the 15th IEEE international conference on data mining workshop, pp 1031–1039

    Google Scholar 

  94. Zhang Y, Pennacchiotti M (2013) Predicting purchase behaviors from social media. In: Proceedings of the 22nd international conference on world wide web, pp 1521–1532

    Google Scholar 

  95. Young Kim E, Kim YK (2004) Predicting online purchase intentions for clothing products. Eur J Mark 38(7):883–897

    Article  Google Scholar 

  96. Liu G, Nguyen TT, Zhao G et al (2016) Repeat buyer prediction for ecommerce. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 155–164

    Google Scholar 

  97. Guo S, Wang M, Leskovec J (2011) The role of social networks in online shopping: information passing, price of trust, and consumer choice. In: Proceedings of the 12th ACM conference on electronic commerce, pp 157–166

    Google Scholar 

  98. Yin H, Cui B, Zhou X et al (2016) Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Trans Inf Syst 35(2):Article 11

    Google Scholar 

  99. Pálovics R, Szalai P, Kocsis L et al (2015) Solving RecSys challenge 2015 by linear models, gradient boosted trees and metric optimization. In: Proceedings of the 9th ACM conference on recommender systems challenge, pp 1–4

    Google Scholar 

  100. Lee J, Kim E, Lee S et al (2019) Frame-to-frame aggregation of active regions in web videos for weakly supervised semantic segmentation. In: Proceedings of the IEEE international conference on computer vision (ICCV 2019), pp 6808–6818

    Google Scholar 

  101. Dewan R, Jing B, Seidmann A (2003) Product customization and price competition on the Internet. Manage Sci 49(8):1055–1070

    Article  Google Scholar 

  102. Bernhardt D, Liu Q, Serfes K (2007) Product customization. Eur Econ Rev 51(6):1396–1422

    Article  Google Scholar 

  103. Basu A, Bhaskaran S (2018) An economic analysis of customer co-design. Inf Syst Res 29(4):786–787

    Article  Google Scholar 

  104. Guo S, Choi TM, Shen B et al (2018) Inventory management in mass customization operations: a review. IEEE Trans Eng Manage 66(3):412–428

    Article  Google Scholar 

  105. Lim KH (2016) Recommending and planning trip itineraries for individual travellers and groups of tourists. In: Proceedings of the 26th international conference on automated planning and scheduling (ICAPS 2016), pp 115–120

    Google Scholar 

  106. Lim KH, Chan J, Karunasekera S et al (2017) Personalized itinerary recommendation with queuing time awareness. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval (SIGIR 2017), pp 325–334

    Google Scholar 

  107. Yu Q, Bouguettaya A (2011) Efficient service skyline computation for composite service selection. IEEE Trans Knowl Data Eng 25(4):776–789

    Article  Google Scholar 

  108. Wagner F, Ishikawa F, Honiden S (2013) Robust service compositions with functional and location diversity. IEEE Trans Serv Comput 9(2):277–290

    Article  Google Scholar 

  109. Zhang F, Hwang K, Khan SU et al (2015) Skyline discovery and composition of multi-cloud mashup services. IEEE Trans Serv Comput 9(1):72–83

    Article  Google Scholar 

  110. Pine BJ, Victor B, Boynton AC (1993) Making mass customization work. Harv Bus Rev 71(5):108–111

    Google Scholar 

  111. Novemsky N, Ravi D, Norbert S et al (2007) The effect of preference fluency on consumer decision making. J Mark Res 44(8):347–357

    Article  Google Scholar 

  112. Gilmore JH, Pine BJ (1997) The four faces of mass customization. Harv Bus Rev 75(1):91–102

    Google Scholar 

  113. Park CW, Jun SY, MacInnis DJ (2000) Choosing what I want versus rejecting what I do not want: an application of decision framing to product option choice decisions. J Mark Res 37(2):187–202

    Article  Google Scholar 

  114. Wang Y, Han D (2012) How customers perceive mass customization: an empirical study based on customer self-efficacy, option presentation and customization satisfaction. J Soft Sci 26(4):140–144

    Google Scholar 

  115. Wu L (2014) Research on multi-objective distribution center location method based on customer satisfaction. J Logist Technol 6:95–97

    Google Scholar 

  116. Yang S, Yu L (2020) Research on express distribution route optimization problem based on genetic algorithm. Mod Inf Technol 4(9):99–103

    Google Scholar 

  117. Fan L, Lv P (2021) Campus takeaway distribution path planning based on improved genetic algorithm. Logist Sci Technol 1:14–19

    Google Scholar 

  118. Huang C, Huang G, Zhu X (2016) Research on the shortest path of delivery based on genetic algorithm. Sci Technol Commun 12(6):94–95

    Google Scholar 

  119. Zhai J, Tai Y (2018) Takeaway delivery route optimization based on time window constraint. Logist Sci Technol 3:15–18

    Google Scholar 

  120. Drops travel city traffic report: in the second quarter of 2018 (2018). https://sts.didistatic.com/official-website/reports/2018 in Q2 city run reports-finally.pdf

  121. Cramer J, Krueger AB (2016) Disruptive change in the taxi business: the case of Uber. Am Econ Rev 106:177–182

    Article  Google Scholar 

  122. Castiglione J, Chang T, Cooper D et al (2016) TNCs today: a profile of San Francisco transportation network company activity. San Francisco County Transportation Authority

    Google Scholar 

  123. Ban JX, Dessouky M, Pang J et al (2018) A general equilibrium model for transportation systems with e-hailing services and flow congestion. Working manuscript, University of Washington, Seattle, WA

    Google Scholar 

  124. Xu Z, Chen Z, Yin Y (2019) Equilibrium analysis of urban traffic networks with ride-sourcing services. SSRN 3422294. https://doi.org/10.2139/ssrn.3422294

  125. Tang L, Zou T, Luo X et al (2017) Research on choice behavior of online car-hailing based on mixed logit model. Transp Syst Eng Inf Technol 18(1):108–114

    Google Scholar 

  126. Yuan L, Wu P (2018) Study on the choice intention and influencing factors of urban residents for online ride-hailing and taxi: logistic analysis based on survey data in Jiangsu Province. J Soft Sci 32(4):120–123

    Google Scholar 

  127. Lu K, Zhou J, Lin X (2019) Research on market pricing of ride-hailing platform considering cross-network externalities. Oper Res Manag 28(7):169–178

    Google Scholar 

  128. Li Y (2018) Research on pricing strategy of online ride-hailing platform based on two-sided market theory. Master’s thesis, Chang’an University, Xi’an

    Google Scholar 

  129. Xu Z, Yan H (2019) Research on parking choice behavior and management strategy of hub online car-hailing. Transp Technol 8(3):155–165

    Google Scholar 

  130. Zhao S, King I, Lyu MR (2016) A survey of point-of-interest recommendation in location-based social networks. arXiv:1607.00647

  131. Cheng C, Yang H, Lyu MR et al (2013) Where you like to go next: successive point-of-interest recommendation. In: Proceedings of 23rd international joint conferences on artificial intelligence, Beijing‚ pp 2605–2611

    Google Scholar 

  132. Zhao S, Zhao T, Yang H et al (2016) STELLAR: spatial-temporal latent ranking for successive point-of-interest recommendation. In: Proceedings of 30th AAAI conferences on artificial intelligence. AAAI Press, Menlo Park, CA, pp 315–322

    Google Scholar 

  133. Liu Q, Wu S, Wang L et al (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of 30th AAAI conferences on artificial intelligence. AAAI Press, Menlo Park, CA, pp 194–200

    Google Scholar 

  134. Johnson CC (2014) Logistic matrix factorization for implicit feedback data. In: Workshop of advances in neural information processing systems. Springer, Berlin

    Google Scholar 

  135. Ahmadian S, Afsharchi M, Meghdadi M (2019) A novel approach based on multi-view reliability measures to alleviate data sparsity in recommender systems. Multimed Tools Appl 1–36

    Google Scholar 

  136. Ahmadian S, Meghdadi M, Afsharchi M (2018) A social recommendation method based on an adaptive neighbor selection mechanism. Inf Process Manage 54:707–725

    Article  Google Scholar 

  137. Ye M, Yin P, Lee WC et al (2009) Exploiting geo-graphical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 325–334

    Google Scholar 

  138. Hang M, Pytlarz I, Neville J (2018) Exploring student check-in behavior for improved point-of-interest prediction. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, KDD’18-exploring student check-in behavior for improved point-of-interest prediction, pp 321–330

    Google Scholar 

  139. Xie M, Yin H, Wang H et al (2016) Learning graph-based POI embedding for location-based recommendation. In: Proceedings of the 25th ACM international on conference on information and knowledge management, Indianapolis, IN, pp 15–24

    Google Scholar 

  140. Liu Y, Pham T, Cong G et al (2017) An experimental evaluation of point-of-interest recommendation in location-based social networks. Proc VLDB Endow 10:1010–1021

    Article  Google Scholar 

  141. Stepan T, Morawski JM, Dick S et al (2016) Incorporating spatial, temporal and social context in recommendations for location-based social networks. IEEE Trans Comput Soc Syst 3:164–175

    Article  Google Scholar 

  142. Aliannejadi M, Rafailidis D, Crestani F (2018) A collaborative ranking model with multiple location-based similarities for venue suggestion. In: Proceedings of the 2018 ACM SIGIR international conference on theory of information retrieval. ACM, New York, pp 19–26

    Google Scholar 

  143. Cheng C, Yang H, King I et al (2016) Fused matrix factorization with geographical and social influence in location-based social networks. In: Twenty-sixth AAAI conference on artificial intelligence. Canada at the Sheraton Centre Toronto, Ontario, pp 17–23

    Google Scholar 

  144. Guo L, Wen Y, Liu F (2019) Location perspective-based neighborhood-aware POI recommendation in location-based social networks. Soft Comput 23:11935–11945

    Article  Google Scholar 

  145. Guo L, Jiang H, Wang X (2018) Location regularization-based POI recommendation in location-based social networks. Information 9:85–95

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hefei University of Technology, Hefei, Anhui, China

    Jie Cao

Authors
  1. Jie Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Xi'an Jiaotong University Press

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cao, J. (2023). Application of Business Big Data Management and Decision Making. In: E-Commerce Big Data Mining and Analytics. Advanced Studies in E-Commerce. Springer, Singapore. https://doi.org/10.1007/978-981-99-3588-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-3588-8_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3587-1

  • Online ISBN: 978-981-99-3588-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation