Interactive calligraphy experience equipment has the characteristics of a large amount of data, various types, and strong homogeneity, which makes it difficult for users to find interesting resources. In this article, a hybrid personalized recommendation algorithm is proposed, which uses collaborative filtering and content‐based recommendation methods in turn to make recommendations. In the initial recommendation, Latent Dirichlet Allocation (LDA) topic model is used to reduce the dimension of high‐dimensional user behavior data and establish a user‐writing theme matrix to reduce inaccurate recommendation caused by high sparsity data in collaborative filtering algorithm. The user interest list is obtained by calculating the similarity between users. Then, on the basis of the preliminary recommendation results, VGG16 model is used to extract the feature vector of the calligraphy image and calculate the similarity between the user's calligraphy words and the primary recommended calligraphy words, thus obtaining the final recommendation results. The experimental results verify the effectiveness and accuracy of the recommendation algorithm, which are better than other recommendation algorithms on the whole, and have important engineering guiding significance.

中文翻译：

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交互式体验设备的个性化推荐混合算法研究

交互式书法体验设备具有数据量大，类型多样，同质性强的特点，用户难以找到有趣的资源。在本文中，提出了一种混合的个性化推荐算法，该算法依次使用协作过滤和基于内容的推荐方法进行推荐。在初始推荐中，使用潜在狄利克雷分配（LDA）主题模型来减少高维用户行为数据的维数，并建立用户编写主题矩阵，以减少协作过滤算法中由高稀疏性数据引起的不正确推荐。通过计算用户之间的相似度来获得用户兴趣列表。然后，根据初步推荐结果，VGG16模型用于提取书法图像的特征向量，并计算出用户的书法文字与主要推荐书法文字之间的相似度，从而获得最终的推荐结果。实验结果验证了该推荐算法的有效性和准确性，总体上优于其他推荐算法，具有重要的工程指导意义。