基于Bayesian Mallows模型的内隐偏好数据的不确定性个性化推荐
Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model
摘要：
在Web应用中，点击数据被广泛地用于产生个性化推荐，其中包含了大量客观的用户偏好信息。目前流行的推荐算法，通常基于矩阵分解，通常具有很高的准确性和实现良好的点击率。然而，推荐项目的多样性，这可以大大提高用户体验，往往被忽视。此外，大多数算法不产生可解释的不确定性量化的建议。在这项工作中，我们提出了贝叶斯锦葵点击数据（BMCD）的方法，它增加了点击数据到兼容的完整的排名向量，通过强制所有点击的项目排名靠前。用户偏好使用Mallow排名模型学习。贝叶斯推断导致每个个人的推荐可解释的不确定性，我们还提出了一种方法，使个性化的建议，基于这种不确定性。通过模拟研究和现实生活中的数据示例，我们证明了与最先进的矩阵分解相比，BMCD可以以相似的准确度进行个性化推荐，同时实现更高水平的多样性，并产生可解释和可操作的不确定性估计。
英文摘要：
Clicking data, which exists in abundance and contains objective user preference information, is widely used to produce personalized recommendations in web-based applications. Current popular recommendation algorithms, typically based on matrix factorizations, often have high accuracy and achieve good clickthrough rates. However, diversity of the recommended items, which can greatly enhance user experiences, is often overlooked. Moreover, most algorithms do not produce interpretable uncertainty quantifications of the recommendations. In this work, we propose the Bayesian Mallows for Clicking Data (BMCD) method, which augments clicking data into compatible full ranking vectors by enforcing all the clicked items to be top-ranked. User preferences are learned using a Mallows ranking model. Bayesian inference leads to interpretable uncertainties of each individual recommendation, and we also propose a method to make personalized recommendations based on such uncertainties. With a simulation study and a real life data example, we demonstrate that compared to state-of-the-art matrix factorization, BMCD makes personalized recommendations with similar accuracy, while achieving much higher level of diversity, and producing interpretable and actionable uncertainty estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
