基于语义相似度的标签优化

Tag optimization based on semantic similarity

为解决社交媒体中标签的缺失、错误等问题,提出一种基于内容相似度和语义相似度的标签优化方法。首先利用TF-IDF(term frequency—inverse document frequency)计算文本间相似度,然后利用文本间相似度与标签相似度的一致性定义了目标函数,最后加入了修正项来减少优化前后用户提供标签的偏差。将目标函数应用到豆瓣电影标签进行优化,并将结果与原标签进行比较分析。与原标签相比,优化后的标签准确性得到了提高。试验结果表明,该方法能够有效地优化标签,有效解决标签缺失和错误等问题。

To effectively solve those problems such as lack and misuse of tags in the social media, a tag optimization method based on content similarity and semantic similarity was proposed. Firstly, TF-IDF （term frequency--inverse document frequency） was used to calculate the text similarity. Afterwards, the objective function was defined by the consistency between text similarity and tag similarity. Finally, correction term was added in optimization process to re- duce the deviation of tags provided by users. The objective function was applied to Douban Movie to optimize movie tags and the results were compared and analyzed with the original tags. The accuracy of the optimized tags was im- proved by comparison. Experimental results showed that the method could effectively optimize tags and solve those problems such as lack and misuse of tags.