论文标题
使用矩阵完成框架在建议中平衡准确性和多样性
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework
论文作者
论文摘要
旨在实现高预测准确性的推荐系统的设计是一个广泛研究的领域。但是，一些研究表明，需要以可接受的准确性来避免单调并改善客户体验的多样化建议。但是，增加的多样性会导致推荐准确性的降低相关。因此，必须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建立的单个联合优化模型来利用可用评级和项目元数据来实现准确性与多样性平衡。与我们的配方不同，大多数现有的作品都提出了一个2阶段模型，这是一种在现有协作过滤技术之上的启发式项目排名方案。在电影推荐系统上进行的实验评估表明，与现有最先进的技术相比，我们的模型可实现给定准确性下降的较高多样性。
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.
