SharkTank Deal Prediction: Dataset and Computational Model
SharkTank Deal Prediction: Dataset and Computational Model
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SharkTank 交易预测：数据集和计算模型
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Tam V. Nguyen
中科院分区：
文献类型：
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作者：
Thomas Sherk;M. Tran;Tam V. Nguyen
鲨鱼缸是一个电视节目，初创企业向一个由五名投资者(鲨鱼)组成的小组推销他们的想法，希望以股权或特许权使用费的形式达成交易，以换取金钱和其他商业福利。自创立以来，鲨鱼缸一直是粉丝、统计学家和商界人士讨论和分析的中心，希望能破解初创企业的密码，找出下一件大事的秘诀。这些讨论和分析大多是以博客、文章和学术研究的形式进行的。然而，一直缺乏完整的数据集和用于进一步分析的计算模型。在这篇文章中，我们调查了影响鲨鱼鱼交易的因素。为此，我们首先通过组合来自多个公共来源的数据来收集一个新的数据集，即鲨鱼鱼缸交易数据集(STDD)。数据集包括每一家初创企业的描述性特征，如产品类别、团队组成、估值、股票发行、出现在那一集的特定鲨鱼，以及州来源。对于计算模型，我们提出了一个新的计算模型来预测初创企业是否与鲨鱼达成交易。我们进行了实验，以证明我们的模型相对于基线的优越性。
SharkTank is a television show where start-ups pitch their idea to a panel of five investors (sharks) in hopes of striking a deal in the form of equity or royalties for money and other business perks. Since its inception, SharkTank has been a center of discussion and analysis for fans, statisticians, and business people alike in hopes of cracking the code to the start-up world and figuring out the formula for the next big ‘thing’. Most of these discussions and analyses have come in the form of blogs, articles, and academic research. However, there has been a lack of complete datasets and application of computational models for further analysis. In this paper, we investigate factors that play into the SharkTank deal. To this end, we first collect a new dataset, SharkTank Deal Dataset (STDD), by combining data from multiple public sources. The dataset includes descriptive features of each start-up such as product category, team composition, valuation, equity offering, specific sharks that appear on that episode, and state origin. For the computational model, we propose a new computational model to predict whether a start-up strikes a deal with a shark. We conduct experiments to demonstrate the superiority of our model over the baselines.