Cognitive biases have been studied in psychology, sociology, and behavioral economics for decades. Traditionally, they have been considered a negative human trait that leads to inferior decision-making, reinforcement of stereotypes, or can be exploited to manipulate consumers, respectively. We argue that cognitive biases also manifest in different parts of the recommendation ecosystem and at different stages of the recommendation process. More importantly, we contest this traditional detrimental perspective on cognitive biases and claim that certain cognitive biases can be beneficial when accounted for by recommender systems. Concretely, we provide empirical evidence that biases such as feature-positive effect, Ikea effect, and cultural homophily can be observed in various components of the recommendation pipeline, including input data (such as ratings or side information), recommendation algorithm or model (and consequently recommended items), and user interactions with the system. In three small experiments covering recruitment and entertainment domains, we study the pervasiveness of the aforementioned biases. We ultimately advocate for a prejudice-free consideration of cognitive biases to improve user and item models as well as recommendation algorithms.

翻译：认知偏差在心理学、社会学和行为经济学领域已被研究数十年。传统上，认知偏差被视为一种负面的人类特质，分别会导致决策质量下降、刻板印象强化，或被用于操纵消费者。我们认为，认知偏差同样显现在推荐生态系统的不同环节以及推荐过程的不同阶段。更重要的是，我们质疑这种对认知偏差的传统有害性观点，并主张当推荐系统充分考虑某些认知偏差时，这些偏差可能产生积极影响。具体而言，我们通过实证证据表明，诸如特征积极效应、宜家效应和文化同质性等偏差，可以在推荐流程的多个组成部分中被观察到，包括输入数据（如评分或辅助信息）、推荐算法或模型（以及由此产生的推荐项目），以及用户与系统的交互。通过涵盖招聘和娱乐领域的三个小型实验，我们研究了上述偏差的普遍性。最终，我们倡导以无偏见的态度考量认知偏差，以改进用户与项目模型以及推荐算法。