Recommender Systems have become the backbone of personalized services that provide tailored experiences to individual users, yet designing new recommendation applications with limited or no available training data remains a challenge. To address this issue, we focus on selecting the universe of items for experimentation in recommender systems by leveraging a recently introduced combinatorial problem. On the one hand, selecting a large set of items is desirable to increase the diversity of items. On the other hand, a smaller set of items enables rapid experimentation and minimizes the time and the amount of data required to train machine learning models. We first present how to optimize for such conflicting criteria using a multi-level optimization framework. Then, we shift our focus to the operational setting of a recommender system. In practice, to work effectively in a dynamic environment where new items are introduced to the system, we need to explore users’ behaviors and interests continuously. To that end, we show how to integrate the item selection approach with active learning to guide randomized exploration in an ongoing fashion. Our hybrid approach combines techniques from discrete optimization, unsupervised clustering, and latent text embeddings. Experimental results on well-known movie and book recommendation benchmarks demonstrate the benefits of optimized item selection and efficient exploration.

推荐系统已成为个性化服务的支柱，为个人用户提供量身定制的体验，但利用有限或没有可用训练数据来设计新的推荐应用程序仍然是一个挑战。为了解决这个问题，我们通过利用最近引入的组合问题，专注于选择用于推荐系统实验的项目范围。一方面，选择大量项目有助于增加项目的多样性。另一方面，较小的项目集可以实现快速实验，并最大限度地减少训练机器学习模型所需的时间和数据量。我们首先介绍如何使用多级优化框架来优化此类相互冲突的标准。然后，我们将重点转移到推荐系统的操作设置上。在实践中，为了在系统引入新项目的动态环境中有效工作，我们需要不断探索用户的行为和兴趣。为此，我们展示了如何将项目选择方法与主动学习相结合，以持续指导随机探索。我们的混合方法结合了离散优化、无监督聚类和潜在文本嵌入的技术。著名电影和书籍推荐基准的实验结果证明了优化项目选择和高效探索的好处。