An increasingly important aspect of designing recommender systems involves
considering how recommendations will influence consumer choices. This paper
addresses this issue by introducing a method for collecting user beliefs about
un-experienced items - a critical predictor of choice behavior. We implemented
this method on the MovieLens platform, resulting in a rich dataset that
combines user ratings, beliefs, and observed recommendations. We document
challenges to such data collection, including selection bias in response and
limited coverage of the product space. This unique resource empowers
researchers to delve deeper into user behavior and analyze user choices absent
recommendations, measure the effectiveness of recommendations, and prototype
algorithms that leverage user belief data, ultimately leading to more impactful
recommender systems. The dataset can be found at
this https URL

该研究论文介绍了一种收集用户对未尝试过物品的信念的方法，该方法对选择行为起到关键作用。作者在 MovieLens 平台上实施了这种方法，生成了包含用户评分、信念和观察到的推荐的丰富数据集。该数据集使研究者能够更深入地了解用户行为，分析在没有推荐的情况下用户的选择，测量推荐的有效性，并设计利用用户信念数据的算法，从而提高推荐系统的影响力。