ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (8): 1813-1823.doi: 10.7544/issn1000-1239.2017.20170188

所属专题: 2017人工智能前沿进展专题

• 人工智能 • 上一篇    下一篇

一种基于协同矩阵分解的用户冷启动推荐算法

高玉凯1,王新华1,郭磊2,陈竹敏3   

  1. 1(山东师范大学信息科学与工程学院 济南 250358);2(山东师范大学管理科学与工程学院 济南 250358);3(山东大学计算机科学与技术学院 济南 250101) (ykgao.cs@gmail.com)
  • 出版日期: 2017-08-01
  • 基金资助: 
    国家自然科学基金项目(61602282,61602284);中国博士后科学基金项目(2016M602181);国家社会科学基金项目(14BTQ049)

Learning to Recommend with Collaborative Matrix Factorization for New Users

Gao Yukai1, Wang Xinhua1, Guo Lei2, Chen Zhumin3   

  1. 1(School of Information Science & Engineering, Shandong Normal University, Jinan 250358);2(School of Management Science & Engineering, Shandong Normal University, Jinan 250358);3(School of Computer Science and Technology, Shandong University, Jinan 250101)
  • Online: 2017-08-01

摘要: 位置服务作为一种信息共享平台,在方便人们交流和共享信息的同时,也因为用户数量的不断增加,而面临着严重的信息过载问题.如何利用推荐技术对信息进行过滤和筛选,帮助用户在位置服务中发现有价值的信息成为近年来研究的热点.但目前已有的推荐算法,在只有消费记录这种隐性数据情况下,针对用户较少活动区域或新用户的推荐效率较低,无法最大化挖掘隐性数据所带的信息.针对以上问题,结合位置服务平台的特点,针对用户冷启动问题,提出了一种结合协同概率矩阵分解与迭代决策树(gradient boosting decision tree, GBDT)的推荐算法.该方法首先使用多层协同概率矩阵分解在多个维度上得到用户潜在特征,然后使用GBDT学习算法对特征和标签进行训练得到用户对项目的偏好,最后使用考虑约束问题的top-N推荐产生推荐列表.在真实数据集上的实验结果表明,与目前较为流行的方法相比,提出的方法能在准确率、F1值上取得较好的结果,能更好地缓解位置服务中的冷启动问题.

关键词: 推荐系统, 位置服务, 概率矩阵分解, 冷启动问题, 约束

Abstract: Location-based service (LBS) as an information sharing platform can help people obtain more useful information. But with the increasing number of users, LBS is faced with a serious problem of information overload. Using the recommender system to filter information and help users to find valuable information has become a hot research topic in recent years. In LBS, only positive implicit feedback is available and user cold-start problem in this scenario is not well studied. Based on the observations, we consider the characteristics of location-based services platform and propose a recommender algorithm, which combines collaborative PMF (probabilistic matrix factorization) with GBDT (gradient boosting decision tree), to solve the cold start problem. The algorithm first use multi probabilistic matrix factorization to learn user latent feature in different dimension, and then use gradient boosting decision tree to train the factor and label to learn the user’s preference, finally use the improved top-N recommender which considers the budget problem to produce the recommendation list. The experimental results on the real data show that the proposed algorithm can achieve better results in accuracy and F1 than other popular methods, and can solve the cold-start problem in LBS recommendation.

Key words: recommender system, location-based service, probabilistic matrix factorization, cold-start problem, budget

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