In the field of information science, how to help users quickly and accurately find the information they need from a tremendous amount of short texts has become an urgent problem. The recommendation model is an important way to find such information. However, existing recommendation models have some limitations in case of short text recommendation. To address these issues, this paper proposes a recommendation model based on semantic features and a knowledge graph. More specifically, we first select DBpedia as a knowledge graph to extend short text features of items and get the semantic features of the items based on the extended text. And then, we calculate the item vector and further obtain the semantic similarity degrees of the users. Finally, based on the semantic features of the items and the semantic similarity of the users, we apply the collaborative filtering technology to calculate prediction rating. A series of experiments are conducted, demonstrating the effectiveness of our model in the evaluation metrics of mean absolute error (MAE) and root mean square error (RMSE) compared with those of some recommendation algorithms. The optimal MAE for the model proposed in this paper is 0.6723, and RMSE is 0.8442. The promising results show that the recommendation effect of the model on the movie field is significantly better than those of these existing algorithms.

中文翻译：

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基于语义特征和知识图的推荐模型

在信息科学领域，如何帮助用户从海量的短文本中快速准确地找到自己需要的信息，已成为一个亟待解决的问题。推荐模型是寻找此类信息的重要途径。然而，现有的推荐模型在短文本推荐的情况下有一些局限性。针对这些问题，本文提出了一种基于语义特征和知识图谱的推荐模型。更具体地说，我们首先选择 DBpedia 作为知识图来扩展项目的短文本特征，并基于扩展的文本获取项目的语义特征。然后，我们计算项目向量，进一步得到用户的语义相似度。最后，基于物品的语义特征和用户的语义相似度，我们应用协同过滤技术来计算预测评分。进行了一系列实验，与一些推荐算法相比，证明了我们的模型在平均绝对误差（MAE）和均方根误差（RMSE）的评估指标中的有效性。本文提出的模型的最优 MAE 为 0.6723，RMSE 为 0.8442。有希望的结果表明，该模型在电影领域的推荐效果明显优于这些现有算法。本文提出的模型的最优 MAE 为 0.6723，RMSE 为 0.8442。有希望的结果表明，该模型在电影领域的推荐效果明显优于这些现有算法。本文提出的模型的最优 MAE 为 0.6723，RMSE 为 0.8442。有希望的结果表明，该模型在电影领域的推荐效果明显优于这些现有算法。