基于标签相似度的数字化书籍资源智能推荐算法

Intelligent Recommendation Algorithm of Digital Book Resources Based on Tag Similarity

针对为帮助读者快速找寻所需的书籍、避免数字化信息出现过载问题,提出基于标签相似度的数字化书籍资源智能推荐算法。首先,根据数字图书馆系统中已录入的用户信息,获取用户特征相似度、用户兴趣相似度,并将其看作综合相似度指标;然后,结合标签相似度指标,获取目标用户书籍资源的相似近邻;最后,将用户浏览过的书籍资源标签构成标签集,通过用户隐式行为评分和线性加权融合相混合的推荐方法,将目标用户喜欢的数字化书籍资源构成推荐列表,并推荐给目标用户。实验结果表明,相比于传统推荐算法,该算法的推荐效果更好。

To help readers quickly find the books they need and avoid overloading digital information,an intelligent recommendation algorithm for digital book resources based on tag similarity is proposed.Firstly,based on the entered user information in the digital library system,the user feature similarity and user interest similarity are obtained and regarded as comprehensive similarity indicators.Then,combined with the tag similarity index,the similarity nearest neighbors of the target user s book resources are obtained.Finally,the tags of the book resources browsed by the user are put into a tag set,and the digital book resources that the target user likes are formed into a recommendation list through a hybrid recommendation method of user implicit behavior scoring and linear weighted fusion,and recommended to the target user.Experimental results show that the proposed algorithm performs better than traditional recommendation algorithms.