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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis
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A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis

机译：基于三元语义分析的社会标签系统中提供推荐的统一框架

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摘要

Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, Web links, products, etc.). Social tagging systems (STSs) can provide three different types of recommendations: They can recommend 1) tags to users, based on what tags other users have used for the same items, 2) items to users, based on tags they have in common with other similar users, and 3) users with common social interest, based on common tags on similar items. However, users may have different interests for an item, and items may have multiple facets. In contrast to the current recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items, and tags. These data are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the higher order singular value decomposition (HOSVD) method and the kernel-SVD smoothing technique. We perform experimental comparison of the proposed method against state-of-the-art recommendation algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.
机译：社交标签是许多用户以关键字的形式添加元数据以对项目（歌曲，图片，Web链接，产品等）进行注释和分类的过程。社交标签系统（STS）可以提供三种不同类型的建议：它们可以向用户推荐1）标签，基于其他用户对相同商品使用的标签，2）对用户商品，基于他们共同的标签其他相似用户，以及3）基于相似商品上的通用标签具有共同社会兴趣的用户。但是，用户可能对项目有不同的兴趣，并且项目可能具有多个方面。与当前的推荐算法相反，我们的方法开发了一个统一的框架来对社交标签系统中存在的三种类型的实体进行建模：用户，项目和标签。这些数据由3阶张量建模，在该张量上使用高阶奇异值分解（HOSVD）方法和核-SVD平滑技术执行多路潜在语义分析和降维。我们通过两个真实数据集（Last.fm和BibSonomy），对建议的方法与最新的推荐算法进行了实验比较。我们的结果表明，通过召回/精确度衡量的有效性有了显着提高。

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