DeepDive: Deep Latent Factor Model for Enhancing Diversity in Recommender Systems

Kriti Kumar, A. Majumdar, M. Chandra
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Abstract

Most collaborative filtering techniques concentrate on increasing the accuracy of business-to-customer recommender systems. Emphasis on accuracy alone leads to repetitive recommendations based on user's past preferences; such predictions pose a problem from both business and user's perspective as they fail to recommend niche items and maintain the user's interest. Incorporating diversity in recommendations can overcome these issues. Most prior studies include diversity by randomizing the item-set predicted by the collaborating filtering technique. These techniques do not have control over the accuracy vs. diversity trade-off; one needs to be mindful that a drastic loss in accuracy is not acceptable from the recommender system. Our work proposes a deep latent factor model with a diversity cost/penalty that allows us to control the trade-off between diversity and accuracy. Experimental results obtained with the Movielens dataset demonstrate the superior performance of our proposed method in providing relevant, novel, and diverse recommendations compared to state-of-the-art techniques; with a slight drop in accuracy, our proposed method provides an improvement in different established measures of diversity.
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DeepDive:用于增强推荐系统多样性的深层潜在因素模型
大多数协同过滤技术集中于提高企业对客户推荐系统的准确性。只强调准确性会导致基于用户过去偏好的重复推荐;从商业和用户的角度来看,这种预测都存在问题,因为它们无法推荐利基商品并保持用户的兴趣。在建议中加入多样性可以克服这些问题。以往的研究大多通过对协同过滤技术预测的项目集进行随机化来实现多样性。这些技术无法控制准确性与多样性之间的权衡;需要注意的是,对于推荐系统来说,准确度的大幅下降是不可接受的。我们的工作提出了一个具有多样性成本/惩罚的深层潜在因素模型,使我们能够控制多样性和准确性之间的权衡。使用Movielens数据集获得的实验结果表明,与最先进的技术相比,我们提出的方法在提供相关、新颖和多样化的推荐方面具有优越的性能;虽然准确度略有下降,但我们提出的方法对不同的已建立的多样性测量方法提供了改进。
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