Design of recommender systems aimed at achieving high prediction accuracy is a widely researched area. However, several studies have suggested the need for diversified recommendations, with acceptable level of accuracy, to avoid monotony and improve customers experience. However, increasing diversity comes with an associated reduction in recommendation accuracy; thereby necessitating an optimum tradeoff between the two. In this work, we attempt to achieve accuracy vs diversity balance, by exploiting available ratings and item metadata, through a single, joint optimization model built over the matrix completion framework. Most existing works, unlike our formulation, propose a 2 stage model, a heuristic item ranking scheme on top of an existing collaborative filtering technique. Experimental evaluation on a movie recommender system indicates that our model achieves higher diversity for a given drop in accuracy as compared to existing state of the art techniques.

中文翻译：

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使用矩阵完成框架平衡建议的准确性和多样性

旨在实现高预测精度的推荐系统设计是一个广泛研究的领域。然而，一些研究表明需要以可接受的准确度进行多样化的推荐，以避免单调并改善客户体验。然而，多样性的增加伴随着推荐准确性的降低。因此需要在两者之间进行最佳权衡。在这项工作中，我们试图通过在矩阵完成框架上构建的单个联合优化模型，利用可用的评级和项目元数据来实现准确性与多样性的平衡。与我们的公式不同，大多数现有作品都提出了一个 2 阶段模型，一种基于现有协同过滤技术的启发式项目排名方案。