The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions. With this information, they can infer the quality of products to reduce the risk of purchase. Specifically, items with high rating scores and good reviews tend to be less risky, while items with low rating scores and bad reviews might be risky to purchase. On the other hand, the purchase behaviors will also be influenced by consumers' tolerance of risks, known as the risk attitudes. Economists have studied risk attitudes for decades. These studies reveal that people are not always rational enough when making decisions, and their risk attitudes may vary in different circumstances. Most existing works over recommendation systems do not consider users' risk attitudes in modeling, which may lead to inappropriate recommendations to users. For example, suggesting a risky item to a risk-averse person or a conservative item to a risk-seeking person may result in the reduction of user experience. In this paper, we propose a novel risk-aware recommendation framework that integrates machine learning and behavioral economics to uncover the risk mechanism behind users' purchasing behaviors. Concretely, we first develop statistical methods to estimate the risk distribution of each item and then draw the Nobel-award winning Prospect Theory into our model to learn how users choose from probabilistic alternatives that involve risks, where the probabilities of the outcomes are uncertain. Experiments on several e-commerce datasets demonstrate that our approach can achieve better performance than many classical recommendation approaches, and further analyses also verify the advantages of risk-aware recommendation beyond accuracy.

中文翻译：

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学习推荐的个性化风险偏好

电子商务的快速发展使人们习惯了网上购物。在电子商务网站上进行购买之前，大多数消费者倾向于依靠评分和评论信息来做出购买决定。通过这些信息，他们可以推断产品的质量以降低购买风险。具体来说，具有高评分和好评的商品往往风险较小，而具有低评分和差评的商品可能有购买风险。另一方面，购买行为也会受到消费者风险承受能力的影响，称为风险态度。几十年来，经济学家一直在研究风险态度。这些研究表明，人们在做决定时并不总是足够理性，在不同的情况下，他们的风险态度可能会有所不同。大多数现有的推荐系统工作在建模时没有考虑用户的风险态度，这可能会导致对用户的推荐不当。例如，向厌恶风险的人推荐风险项目或向寻求风险的人推荐保守项目可能会导致用户体验的降低。在本文中，我们提出了一种新的风险感知推荐框架，该框架结合了机器学习和行为经济学，以揭示用户购买行为背后的风险机制。具体来说，我们首先开发统计方法来估计每个项目的风险分布，然后将获得诺贝尔奖的前景理论引入我们的模型，以了解用户如何从涉及风险的概率替代方案中进行选择，其中结果的概率是不确定的。