融合用户属性与项目流行度的用户冷启动推荐模型  

User Cold Start Recommendation Model Integrating User Attributes and Item Popularity

作　　者：韩立锋[1] 陈莉[1] HAN Li-feng;CHEN Li(School of Information Science&Technology,Northwest University,Xi’an 710127,China)

机构地区：[1]西北大学信息科学与技术学院,西安710127

出　　处：《计算机科学》2021年第2期114-120,共7页Computer Science

基　　金：陕西省重点研发计划项目(2019ZDLGY10-01)。

摘　　要：冷启动一直是推荐系统领域中被密切关注的问题,针对新注册用户冷启动的问题,文中提出了一种融合用户人口统计学信息与项目流行的推荐模型。首先对训练集用户进行聚类,将训练集用户划分为若干类。然后计算新用户与所属类别中其他用户之间的距离,选择其近邻用户集,在评分计算时综合考虑项目流行度对推荐效果的影响,进而为目标用户推送感兴趣的节目。最后在经典推荐系统数据集中对所提模型进行验证。实验结果表明,该模型明显优于传统协同过滤算法,并在一定程度上解决了冷启动问题。Cold start has always been a closely watched issue in the field of recommendation systems.Aiming at the problem of cold start for newly registered users,this paper proposes a recommendation model that integrates user demographic information and item popularity.The training set users are divided into several categories by clustering the training set users,and then the distance between the new user and other users in the category is calculated,and the neighboring user set is selected.When calcula-ting the score,we consider comprehensively the impact of popularity,and then push the programs of interest to target users.Finally,the proposed model is verified on the classic recommendation system data set.The results show that the model is significantly better than the traditional collaborative filtering algorithm and has a certain mitigation effect on the cold start problem.

关 键 词：推荐系统 用户冷启动 社会统计学信息 协同过滤 项目流行度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]