基于直觉模糊集的伯努利矩阵分解推荐算法
作者:
作者单位:

天津大学

作者简介:

通讯作者:

中图分类号:

TP391

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Intuitionistic Fuzzy Sets Based Bernoulli Matrix Factorization Recommendation Algorithm
Author:
Affiliation:

Tianjin University

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    现有的基于矩阵分解的协同过滤推荐算法主要从定量的角度,利用用户的评分信息来评估模型表现,而从未从定性的角度来描述用户的不确定决策信息。基于此,本文首次从用户偏好模糊概率的角度,提出了一种基于直觉模糊集的伯努利矩阵分解推荐算法来为目标用户进行Top-n推荐。首先,根据用户偏好特征和直觉模糊集定义,将用户评分矩阵划分为隶属度矩阵、非隶属矩阵和犹豫度矩阵。其次,借助伯努利矩阵分解模型来对这些矩阵进行并行拟合,得到最优的潜在特征向量对,并将其内积按比例划分,从而获得目标用户对未评分项目偏好程度的直觉模糊数。最后,根据直觉模糊数排序规则,确定最终推荐列表。实验结果在公开数据集上显示,本文提出的方法在项目排序指标上均优于其对比的方法,有效地提高了推荐质量。

    Abstract:

    Existing matrix factorization based collaborative filtering recommendation algorithms mainly utilize users’ ratings to evaluate model performance from a quantitative perspective, and never describe users’ uncertain decision information from a qualitative perspective. Therefore, this paper proposes a Bernoulli matrix factorization recommendation model based on intuitionistic fuzzy sets (IFSs) to make Top-n recommendations for active users from the perspective of fuzzy probability of user preferences. Firstly, the user-item rating matrix is divided into the membership matrix, non-membership matrix, and hesitancy matrix according to user preference features and the definition of IFSs. Subsequently, the Bernoulli matrix factorization (BeMF) is adopted to fit these matrices in parallel to obtain the optimal latent feature vectors, and their inner products are divided proportionally to get the intuitionistic fuzzy number (IFV) of the active users’ preference degree for unrated items. Finally, the recommendation lists are determined according to the ranking rule of the IFV. Experimental results on several benchmark datasets show that the proposed model outperforms other methods in terms of item ranking metrics and effectively improves the recommendation quality.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2022-03-06
  • 最后修改日期:2022-12-29
  • 录用日期:2022-06-10
  • 在线发布日期: 2022-06-13
  • 出版日期: