基于用户画像相似性的电影评分预测模型

doi:10.3969/j.issn.0255-8297.2025.02.003

摘要/Abstract

摘要： 协同过滤算法在推荐算法中应用广泛，如何实现用户聚类并发现更相似的邻居集合一直是协同过滤推荐算法的研究重点。为了有效提高该类算法分类和预测的准确性，本文提出了一种基于用户画像相似性的电影推荐算法。首先，基于电影内容特征的标签集合，计算用户评分在不同电影内容标签上的频数，建立基于电影内容标签的用户偏好画像矩阵。然后通过该矩阵计算用户间的相似性并进行用户复杂网络建模，计算用户在该网络中的中心性权重。最后，结合用户网络K-core分解得到用户网络的社区权重，并利用邻居用户的中心性权重和社区权重改进评分预测。实验结果表明，该算法在评测指标预测准确性和分类准确性上分别提高2.72%和3.17%，验证了基于用户画像相似性进行复杂网络建模对推荐系统信息利用的有效性。

关键词: 用户画像, 协同过滤, 相似性, 复杂网络, 分类准确性

Abstract: Collaborative filtering algorithms are widely used in recommendation systems, with a key research focus on how to achieve effective user clustering and identify more accurate sets of similar neighbors. One of the key research focuses is to achieve user clustering and identify more similar neighbor sets. To enhance the accuracy of classification and prediction in these algorithms, this paper proposes a movie recommendation algorithm based on user profile similarity. First, based on a tag set of movie content features, a user preference profile matrix is constructed by calculating the frequency of user ratings among different movie content tags. Then, user similarity is calculated through this matrix, and a user complex network is modeled to determine the centrality weight of users within the network. Finally, the community weight is obtained through K-core decomposition of the user network. The rating predictions are improved by integrating the centrality weight and community weight of neighboring users. Experimental results show that the proposed algorithm improves prediction accuracy and classification accuracy by 2.72% and 3.17%, respectively. These results validate the effectiveness of complex network modeling based on user profile similarity for enhancing information utilization in recommendation systems.

Key words: user profile, collaborative filtering, similarity, complex network, classification accuracy

中图分类号:

TP391

艾均, 李明浩, 苏湛. 基于用户画像相似性的电影评分预测模型[J]. 应用科学学报, 2025, 43(2): 222-233.

AI Jun, LI Minghao, SU Zhan. A Movie Rating Prediction Model Leveraging User Profile Similarity[J]. Journal of Applied Sciences, 2025, 43(2): 222-233.

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