推荐系统中谁可以协同新用户？

计算机科学 ›› 2015, Vol. 42 ›› Issue (Z11): 80-82.

推荐系统中谁可以协同新用户？

张莉,余磊

对外经济贸易大学信息学院北京100029,对外经济贸易大学信息学院北京100029

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家社科基金项目(13BTQ027)资助

Who Can Collaborate New Users in Recommendation System？

ZHANG Li and YU Lei

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 协同过滤作为被成功应用于推荐系统的技术之一,得到了各领域学者的关注。然而随着网络平台新用户和项目的不断增加,协同推荐面临严重的“冷启动”问题的挑战。首先基于用户流行度和长尾分布建立用户推荐能力的度量方法,然后利用用户推荐能力筛选出一个用于推荐的全局核心用户子集,来解决推荐系统的“冷启动”问题。实验结果显示,将构建的全局核心用户集合用于协同推荐,在不降低推荐效果的基础上,可显著降低寻找相似用户的时间复杂度,因而可以将其用于解决推荐实时性问题。

关键词: 协同过滤,核心用户,长尾分布,用户流行度

Abstract: As a successful technology used in the recommender system,collaborative filtering has been widly concerned by scholars in various fields.However,with the increasing of new users and items,collaborative recommendation is facing serious challenge of “cold start”.This study measured the recommending ability of user based on popularity and long-tailed distribution,and then constructed a global core user set for recommadition using user popularity,which can be used to solve “cold start” problems in recommendation systems.In additional,experimental results show that the core use set used for collaborative recommending can reduce complexity of looking for similar users without lowing the recommendation performance.So it also can be used to improve real-time recommendation.

Key words: Collaborative filtering,Core users,Long-tailed distribution,User popularity

张莉,余磊. 推荐系统中谁可以协同新用户？[J]. 计算机科学, 2015, 42(Z11): 80-82. https://doi.org/

ZHANG Li and YU Lei. Who Can Collaborate New Users in Recommendation System？[J]. Computer Science, 2015, 42(Z11): 80-82. https://doi.org/

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