基于改进用户画像的协同过滤推荐算法研究
Research on Collaborative Filtering Recommendation Algorithm Based on Improved User Portraits
侯萌 1王国鹏 2宋丽哲 2王皓月 1司占军1
作者信息
- 1. 天津科技大学人工智能学院,天津 300457
- 2. 国家开放大学,北京 100039;数字化学习技术集成与应用教育部工程研究中心,北京 100039
- 折叠
摘要
随着大数据时代的来临,信息过载现象也日渐严重.本研究针对推荐系统中经常遇到的用户评分矩阵稀疏的问题,提出基于改进用户画像的协同过滤推荐算法.首先,根据用户特性构建画像标签体系.本研究提出使用用户基本信息、项目基本信息创建用户画像标签并构建多维度用户画像,利用TF-IDF算法确定用户-项目特征标签权重.其次,分别使用基于用户画像的协同过滤算法与基于用户的协同过滤算法加权计算用户相似度,通过调和权重得到用户最终相似度.最后,利用Top-N进行个性化推荐.通过MovieLens-1M数据集进行验证,发现本研究算法推荐结果的准确率以及召回率相比其单一方法(基于用户画像的协同过滤推荐算法和基于用户的协同过滤推荐算法)均有所提升.
Abstract
With the arrival of the big data era,the phenomenon of information overload is becoming increasingly severe.In response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filtering recommendation algorithm was proposed based on improved user profiles in this study.Firstly,a profile labeling system was constructed based on user characteristics.This study proposed that user profile labels should be created using basic user information and basic item information,in order to construct multidimensional user profiles.TF-IDF algorithm was used to determine the weights of user-item feature labels.Secondly,user similarity was calculated by weighting both profile-based collaborative filtering and user-based collaborative filtering algorithms,and the final user similarity was obtained by harmonizing these weights.Finally,personalized recommendations were generated using Top-N method.Validation with the MovieLens-1M dataset revealed that this algorithm enhances both recommendation Precision and Recall compared to single-method approaches (recommendation algorithm based on user portrait and user-based collaborative filtering algorithm).
关键词
协同过滤/用户画像/推荐算法/相似度Key words
Collaborative filtering/User profiling/Recommender system/Similarity引用本文复制引用
出版年
2024
NETL
NSTL
万方数据