摘要
本文以改善算法效果为目标,从用户的心理需求出发,定位用户的隐性角色群体,来对个性化的推荐算法展开研究.从理论的角度来看,本文研究有效保证了推荐系统的多样性要求,并在一定程度上提升了算法的准确性,针对偏好演化现象扩展隐性偏好的相关理论,通过在现实数据中的验证,实验结果显示多项实验评价指标得到显著提升,不仅为推荐系统提供了理论基础和借鉴作用,还能提高推荐结果的准确率,具有广泛的应用前景.从实践的角度来看,本文对用户的分类不再局限于普通的社会属性,能够更深层次地挖掘出用户的心理需求,得到更准确、多样的推荐结果,提高用户的满意度,改善用户体验,而企业则可以引导用户的兴趣变动,提高用户的忠诚度和价值,改善用户生命周期,提高企业利润.
Abstract
This article aims to improve the effectiveness of the algorithm,starts from the psychological needs of users,locates the implicit role group of users,and researches the personalized recommendation algorithms.From a theoretical point of view,the research in this paper effectively ensures the diversity requirements of recommendation systems and improves the accuracy of algorithms to a certain extent.It expands the relevant theory of implicit preference to address the phenomenon of preference evolu-tion.Through verification in real data,multiple experimental evaluation indicators have been significantly improved.This not only provides a theoretical basis and reference for recommendation systems,but also improves the accuracy of recommendation results.It has broad application prospects.From a practical point of view,the classification of users in this article is no longer limited to ordinary social attributes,but can further explore users'psychological needs,obtains more accurate and diverse rec-ommendation results,improves user satisfaction and experience.Enterprises can guide users to change their interests,increase their loyalty and value,improve their lifecycle,and increase their profits.
基金项目
国家自然科学基金资助项目(72271037)
中央高校基本科研业务费专项资金资助项目(3132019353)
国家科技期刊平台
NSTL
万方数据