用ChatGPT分析电影描述语义，提升推荐精准度。
Proposing a Semantic Movie Recommendation System Enhanced by ChatGPT's NLP Results
- 用ChatGPT解析电影简介的语气与语义，构建知识图谱。
- 新方法在准确率上显著优于依赖出版商提供的显式类型。
- 适合关注语义理解与个性化推荐的研究者与开发者。
网络推荐系统在电影行业的重要性日益凸显，面对海量影片选择，如何帮助用户快速找到契合兴趣的内容成为关键。传统方法依赖用户行为数据与显式标签，但难以捕捉深层偏好。本研究提出一种基于语义信息的知识图谱构建新方法，利用ChatGPT对电影简介进行自然语言处理，提取其情感基调与语义特征。实验表明，该方法在推荐准确率上显著优于仅使用出版商提供的显式类型。通过引入大语言模型的语义理解能力，系统能更精准捕捉用户偏好，提升个性化推荐效果。
原文摘要 · Abstract (English)
The importance of recommender systems on the web has grown, especially in the movie industry, with a vast selection of options to watch. To assist users in traversing available items and finding relevant results, recommender systems analyze operational data and investigate users' tastes and habits. Providing highly individualized suggestions can boost user engagement and satisfaction, which is one of the fundamental goals of the movie industry, significantly in online platforms. According to recent studies and research, using knowledge-based techniques and considering the semantic ideas of the textual data is a suitable way to get more appropriate results. This study provides a new method for building a knowledge graph based on semantic information. It uses the ChatGPT, as a large language model, to assess the brief descriptions of movies and extract their tone of voice. Results indicated that using the proposed method may significantly enhance accuracy rather than employing the explicit genres supplied by the publishers.
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