基于用户画像和链路预测的电影个性化推荐研究  

Research on Personalized Movie Recommendation Based on User Portrait and Link Prediction

作　　者：周晨曦 赵天驰 张玲玲 ZHOU Chenxi;ZHAO Tianchi;ZHANG Lingling(School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190;Key Laboratory of Big Data Mining and Knowledge Management,Chinese Academy of Sciences,Beijing 100190;MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS,Beijing 100190)

机构地区：[1]中国科学院大学经济与管理学院,北京100190 [2]中国科学院大数据挖掘与知识管理重点实验室,北京100190 [3]中国科学院大学数字经济监测预测预警与政策仿真教育部哲学社会科学实验室(培育),北京100190

出　　处：《系统科学与数学》2024年第6期1586-1607,共22页Journal of Systems Science and Mathematical Sciences

基　　金：国家自然科学基金面上项目(72071194);中国科学院大学数字经济监测预测预警与政策仿真教育部哲学社会科学实验室(培育)基金(E2810801)资助课题。

摘　　要：当今电影市场上海量的影片资源使用户面临选择困境,高效的电影推荐系统对提升用户体验以及电影服务商的市场竞争力具有重要作用.如何融合多种数据源进行个性化推荐,在提升算法精度的同时平衡多样性是电影推荐系统面临的挑战,针对该问题的研究具有重要的理论和现实意义.用户画像可以从多维度刻画丰富的用户特征,更好地理解用户兴趣和行为,链路预测从网络拓扑结构视角建模具有独特优势,二者的融合为解决上述问题提供了可能性.为此,该研究提出了一种基于用户画像和链路预测的电影个性化推荐算法.该算法以电影推荐为背景,区分了用户的行为和领域兴趣相似性,融合多源数据构建用户画像,作为网络外部信息融入链路预测过程中,有助于提升算法精度.此外,算法还从稀缺性视角改进了二分图投影中用户兴趣相似性的计算,同时衡量了链接在推荐中的促进或抑制作用强度,使推荐结果更加新颖和个性化,流行度偏差问题得到一定程度的改善.最后,文章基于两个MovieLens数据集对提出的推荐算法进行了实验验证,结果表明与代表性算法相比,文章提出的算法不仅在准确性上有显著提升,在与多样性相关的指标上也有明显的优势.此外,抽象出的用户画像能够帮助推荐平台了解其用户群,从而制定出更为科学的营销与管理策略.The overwhelming selection of movies on the market nowadays makes it difficult for users to make a decision.An efficient movie recommendation system plays a significant role in improving user experience and the market competitiveness of movie service providers.The challenge lies in how to integrate multiple data sources for personalized recommendations while balancing algorithm accuracy and diversity.Research on this issue is of great theoretical and practical significance.User portraits can depict rich user characteristics from multiple dimensions,helping us to better understand user interests and behaviors.Meanwhile,link prediction offers special benefits when modeling from a network topology standpoint.The integration of them provides a possibility to solve the above issues.Therefore,this study proposes a novel user portrait and link prediction-based personalized recommendation algorithm called UPLPR.The algorithm is designed under the background of movie recommendation.It distinguishes between the interest similarity that reflects in user behavior and genre domain.By abstracting user portraits from multiple data sources and integrating them into the link prediction process as external information of the network,the accuracy of the algorithm can be improved.Furthermore,from the perspective of scarcity,the algorithm improves the calculation of interest similarity between users in bipartite graph projection and evaluates the promoting or inhibiting effect of links in the recommendation process.Such consideration improves the novelty and personalization of the recommendation and mitigates the popularity bias problem to some extent.Finally,experiments were conducted on two Movie-Lens datasets to verify the proposed recommendation algorithm.Results show that compared with representative algorithms,the algorithm proposed in this paper not only achieved significant performance in accuracy but also demonstrated obvious advantages in diversity-related indicators.Additionally,the abstracted user portraits can help recom

关 键 词：个性化推荐 链路预测 用户画像 二分图 稀缺性视角 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]