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- W2812291259 abstract "Lowest unique bid auction (LUBA) sites are gaining popularity on the Internet in recent years. In this paper, we study LUBA with resubmission in discrete bid spaces. A long-standing goal in the field of Internet auction is to develop agents that can perceive and understand the strategy information behind the mechanism and can guide us to behave in a fast, frugal and smart way. We marry ideas from recurrent neural network and data to learn a generative model for generating winning bid sequences. A sequence of winning bids in Internet auctions can be viewed as a sequence of events and modeled by generative models. We learn a model that is able to capture the long dependencies in a winning bid sequence. The generated data obtained from our model and the ground truth dataset share similar distributions." @default.
- W2812291259 created "2018-07-19" @default.
- W2812291259 creator A5073000035 @default.
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- W2812291259 date "2018-06-01" @default.
- W2812291259 modified "2023-09-23" @default.
- W2812291259 title "Generative model of bid sequences in lowest unique bid auctions" @default.
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- W2812291259 doi "https://doi.org/10.1109/ccdc.2018.8407671" @default.
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