隐性角色下的协同推荐算法

Collaborative Recommendation Algorithm with Implicit Roles

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。

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 evolution.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 recommendation 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.