Balancing accuracy and diversity in recommendations using matrix completion framework
Balancing accuracy and diversity in recommendations using matrix completion framework
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DOI：
10.1016/j.knosys.2017.03.023
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
Anupriya Gogna;A. Majumdar
中科院分区：
文献类型：
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作者：
Anupriya Gogna;A. Majumdar
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，一些研究表明需要多样化的推荐，并具有可接受的准确度，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，我们尝试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。与我们的表述不同，大多数现有的工作提出了一个两阶段模型——基于现有协同过滤技术的启发式项目排名方案。对电影推荐系统的实验评估表明，与现有的最先进技术相比，我们的模型在给定的准确性下降的情况下实现了更高的多样性。
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-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.