Abstract: 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.

Key words: label similarity, digital book resources, intelligent recommendation, mixed recommendation, similar neighbors