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On improving aggregate recommendation diversity and novelty in folksonomy-based social systems

机译：基于民俗分类的社会系统中总体推荐多样性和新颖性的改进

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摘要

Benefit from technical advances in the Internet of Things, many social media applications relative to folksonomy have become ubiquitous. The size and complexity of folksonomy-based systems can unfortunately lead to information overload and reduced utility for users. Consequentially, the increasing need for recommender services from users has arisen. Many efforts have been made to address recommendation accuracy as well as other issues with respect to personalized recommendation in such systems. A key challenge facing these systems is that the most useful individual recommendations are to be found among diverse niche resources while increasing diversity most often compromises accuracy. In this paper, we introduce a simple yet elegant method-Diversity-aware Personalized PageRank (DaPPR)-to address this challenge from the aggregate perspective. DaPPR exploits a balance factor to adjust the influence of a personalized ranking vector and a unified non-personalized ranking vector based on PageRank. By this, it can reduce the impact of resource popularity on recommendations and then generate more diverse and novel recommendations to users. A hybrid DaPPR model that combines two ranking processes on the user-resource and the resource-tag bipartite graphs is specifically designed to meet the requirements in folksonomy-based systems. According to solid experiments, our proposed method yields better results balancing both aggregate accuracy and aggregate diversity (novelty). Improvements of all performance metrics are also obtained compared with the existing algorithms.
机译：得益于物联网技术的进步，许多与民俗疗法相关的社交媒体应用已变得无处不在。不幸的是，基于民俗分类的系统的大小和复杂性可能导致信息过载并降低用户的实用性。因此，用户对推荐服务的需求不断增长。在这种系统中，已经做出许多努力来解决推荐准确性以及关于个性化推荐的其他问题。这些系统面临的主要挑战是，在多样化的利基资源中找到最有用的个人建议，而增加多样性通常会损害准确性。在本文中，我们介绍了一种简单而优雅的方法-多样性感知的个性化PageRank（DaPPR）-从总体角度解决这一挑战。 DaPPR利用平衡因子来调整基于PageRank的个性化排名向量和统一的非个性化排名向量的影响。这样，它可以减少资源受欢迎程度对推荐的影响，然后为用户生成更加多样化和新颖的推荐。混合DaPPR模型将用户资源和资源标签二部图上的两个排序过程结合在一起，专门设计用于满足基于民俗分类法的系统中的需求。根据可靠的实验，我们提出的方法在总体准确性和总体多样性（新颖性）之间取得了更好的结果。与现有算法相比，还可以获得所有性能指标的改进。

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