Abstract
Online bidding systems are a well-known method of satisfying online buyers’ and sellers’ expectations since they allow both parties to buy and sell goods at competitive prices. A live auction is used to implement the online bidding system, allowing multiple bidders to participate at once. Together, these bidders may place a bid on any item. You can sell anything on the website with this app from your home or a store. It is being created with the intention of making the system dependable, simple, and quick. Everyone can now take part in an auction while relaxing in their own homes. Despite the popularity of internet auctions, there are numerous dishonest buying or selling practises that might take place. One of the trickiest forms of auction fraud to spot among all of them is shill bidding. Shill bidding is when a seller participates in his or her own auction while purposefully placing a false bid to raise the price at the end. The seller may do this on his or her own, or a third party may work along with the seller to submit fictitious bids on the seller’s behalf.
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References
Aggarwal, C.C., Yu, P.S.: Online auctions: there can be only one. In: 2009 IEEE Conference on Commerce and Enterprise Computing (2009)
Dong, F., Shatz, S.M., Xu, H.: Combating online in-auction fraud: clues techniques and challenges. Comput. Sci. Rev. 3(4), 245–258 (2009)
Trevathan, J., Read, W.: Undesirable and fraudulent behaviour in online auctions. In: Proceedings of International Conference on Security and Cryptograpghy, pp. 450–458 (2006)
Ghani, R., Simmons, H.: Predicting the end-price of online auctions. In: Proceedings of the International (2004)
Wang, S., Jank, W., Shmueli, G.: Explaining and forecasting online auction prices and their dynamics using functional data analysis. J. Bus. Econ. Stat. 26(2), 144–160 (2008). https://doi.org/10.1198/073500106000000477
Chan, N.H., Li, Z.R., Yau, C.Y.: Forecasting online auctions via self-exciting point processes. J. Forecast. 33(7), 501–514 (2014). https://doi.org/10.1002/for.2313
Kaur, P., Goyal, M., Lu, J.: Pricing analysis in online auctions using clustering and regression tree approach. In: Cao, L., Bazzan, A.L.C., Symeonidis, A.L., Gorodetsky, V.I., Weiss, G., Yu, P.S. (eds.) ADMI 2011. LNCS (LNAI), vol. 7103, pp. 248–257. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27609-5_16
Pinto, T., Sousa, T.M., Praça, I., Vale, Z., Morais, H.: Support Vector Machines for decision support in electricity markets׳ strategic bidding. Neurocomputing 172, 438–445 (2016). https://doi.org/10.1016/j.neucom.2015.03.102
Matsuo T., Ito T., Shintani T.: An approach to avoiding shill bids based on combinatorial auction in volume discount. In: First International Workshop on Rational, Robust, and Secure Negotiation Mechanisms in Multi-Agent Systems, pp. 25–38 (2005)
Lei, B., Zhang, H., Chen, H., Liu, L., Wang, D.: A k-means clustering based algorithm for shill bidding recognition in online auction. In:2012 24th Chinese Control and Decision Conference (CCDC), pp. 939–943. Taiyuan, China (2012). https://doi.org/10.1109/CCDC.2012.6244147
https://www.econport.org/content/handbook/auctions/historyofauctions.html
https://www.sutori.com/en/story/history-of-online-auctions--QSFnJLMNZGNX1c661rm4Nwex
Ganguly, S., Sadaoui, S.: Online detection of shill bidding fraud based on machine learning techniques. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds.) IEA/AIE 2018. LNCS (LNAI), vol. 10868, pp. 303–314. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92058-0_29
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Mudlapur, C., Jain, S., Mittal, S., Jain, P., Neerugatti, V. (2023). Live Bidding Application: Predicting Shill Bidding Using Machine Learning. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science(), vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_29
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