用认知启发算法克服标签推荐方法和真实世界的Folksonomy结构之间的不平衡
Overcoming the Imbalance Between Tag Recommendation Approaches and Real-World Folksonomy Structures with Cognitive-Inspired Algorithms
摘要：
在本文中，我们研究了当前国家的最先进的标签推荐算法和现实世界的社会标签系统的大众分类结构之间的不平衡。虽然像FolkRank这样的算法是为密集的大众分类结构设计的，但大多数社交标签系统都表现出稀疏的性质。为了克服这种不平衡，我们表明，认知启发的算法，其中模型的标签词汇的用户在一个认知合理的方式，可以是有帮助的。我们目前的方法是通过实现认知架构ACT-R的激活方程来实现这一点的，ACT-R决定了人类记忆中单元的有用性（例如，标签）。从这个意义上说，我们的长期研究目标是设计混合推荐方法，它结合了两个世界的优点，以适应当前的设置（即，稀疏对密集）。
英文摘要：
In this paper, we study the imbalance between current state-of-the-art tag recommendation algorithms and the folksonomy structures of real-world social tagging systems. While algorithms such as FolkRank are designed for dense folksonomy structures, most social tagging systems exhibit a sparse nature. To overcome this imbalance, we show that cognitive-inspired algorithms, which model the tag vocabulary of a user in a cognitive-plausible way, can be helpful. Our present approach does this via implementing the activation equation of the cognitive architecture ACT-R, which determines the usefulness of units in human memory (e.g., tags). In this sense, our long-term research goal is to design hybrid recommendation approaches, which combine the advantages of both worlds in order to adapt to the current setting (i.e., sparse vs. dense ones).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
