Recommender systems (RSs) have become key components driving the success of e-commerce and other platforms where revenue and customer satisfaction is dependent on the user’s ability to discover desirable items in large catalogues. As the number of users and items on a platform grows, the computational complexity and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RSs, while effective in providing suggestions for established users and items, are known for their poor performance for the new user and new item (cold-start) problems. Stereotypical modelling of users and items is a promising approach to solving these problems. A stereotype represents an aggregation of the characteristics of the items or users which can be used to create general user or item classes. We propose a set of methodologies for the automatic generation of stereotypes to address the cold-start problem. The novelty of the proposed approach rests on the findings that stereotypes built independently of the user-to-item ratings improve both recommendation metrics and computational performance during cold-start phases. The resulting RS can be used with any machine learning algorithm as a solver, and the improved performance gains due to rate-agnostic stereotypes are orthogonal to the gains obtained using more sophisticated solvers. The paper describes how such item-based stereotypes can be evaluated via a series of statistical tests prior to being used for recommendation. The proposed approach improves recommendation quality under a variety of metrics and significantly reduces the dimension of the recommendation model.

推荐系统 (RS) 已成为推动电子商务和其他平台成功的关键组件，在这些平台中，收入和客户满意度取决于用户在大型目录中发现所需商品的能力。随着平台上用户和项目数量的增长，计算复杂度和稀疏性问题对任何推荐算法构成了重要挑战。此外，研究最广泛的基于过滤的 RS 虽然可以有效地为已建立的用户和项目提供建议，但因其对新用户和新项目（冷启动）问题的性能不佳而闻名。用户和项目的定型建模是解决这些问题的一种很有前途的方法。构造型表示项目或用户特征的聚合，可用于创建一般用户或项目类。我们提出了一套自动生成刻板印象的方法来解决冷启动问题。所提出方法的新颖之处在于发现独立于用户到项目评级的刻板印象在冷启动阶段提高了推荐指标和计算性能。由此产生的 RS 可以与任何机器学习算法一起用作求解器，并且由于速率不可知的刻板印象而提高的性能增益与使用更复杂的求解器获得的增益正交。该论文描述了如何在用于推荐之前通过一系列统计测试来评估这种基于项目的刻板印象。