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An Information Recommendation System Focusing on Social Bookmarking

机译：专注于社会书签的信息推荐系统

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摘要

This paper proposes an information recommendation system focusing on personal classification space based on bookmarks. This study handles the problem where the standard collaborative filtering method cannot offer recommendations with a better level of precision. This paper improves the collaborative filtering algorithm for users of social bookmark services. In this research, the classification-based method is proposed as the recommendation method, and Recall-DCG and Precision-DCG are proposed as evaluation scales. A user's bookmarks are placed on his/her own classifying space made of tags. These bookmarks are transformed into a degree of similarity for recommendations. The degree is used to compare the personal classifying space with another's space. Comparison with previous studies confirms the superiority of the method based on space classification. In particular, where the cos distance with the distribution weight added is used as similarity between items. The cos distance with the matching weight added is designed with the expectation of the effect of striking up the characteristics of other information. This proposed method shows a significant superiority in almost all experiments performed.
机译：本文提出了一种专注于基于书签的个人分类空间的信息推荐系统。本研究处理标准协作滤波方法不能提供更好的精度水平的建议的问题。本文提高了社会书签服务用户的协同过滤算法。在该研究中，提出了基于分类的方法作为推荐方法，并提出了Recall-DCG和Precision-DCG作为评估尺度。用户的书签放在他/她自己的分类空间上，由标签制成。这些书签转变为建议的相似程度。该程度用于将个人分类空间与另一个空间进行比较。与先前研究的比较证实了基于空间分类的方法的优越性。特别地，其中添加了与分布重量的COS距离用作物品之间的相似性。添加了与匹配重量的COS距离是为了预期敲击其他信息特征的效果。该提出的方法在几乎所有实验中显示出显着的优势。

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