A Random Walk Model for Item Recommendation in Social Tagging Systems

Zhu Zhang, D. Zeng, A. Abbasi, Jing Peng, Xiaolong Zheng
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引用次数: 47

Abstract

Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users’ preferences on an item similarity graph and spreading items’ influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation.
社会标签系统中项目推荐的随机游走模型
社会标记作为一种新的信息组织和发现方法,已在许多Web 2.0应用程序中广泛采用。由用户贡献的用于注释各种Web资源或项目的标签提供了一种可以被推荐系统利用的新型信息。然而,用户、项目和标签之间三元交互数据的稀疏性限制了基于标签的推荐算法的性能。在本文中,我们提出通过在三元交互图上应用随机漫步来探索用户和项目之间的传递关联来处理社会标签中的稀疏性问题。本文中的传递关联是指长度大于1的任意两个节点之间的链接路径。利用这些传递关联可以更精确地度量两个实体(例如,user-item、user-user和item-item)之间的相关性。一种类似pagerank的算法通过在项目相似图上传播用户偏好和在用户相似图上传播项目影响来探索这些传递关联。对三个真实数据集的实证评估表明,我们的方法可以有效地缓解稀疏性问题,提高项目推荐的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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