Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 530-534.DOI: 10.11772/j.issn.1001-9081.2019101791

• CCF Bigdata 2019 • Previous Articles     Next Articles

Item-based unified recommendation model

Kai DENG1, Jiajin HUNAG2(), Jin QIN1   

  1. 1.School of Computer Science and Technology,Guizhou University,Guiyang Guizhou 550025,China
    2.International WIC Institute,Beijing University of Technology,Beijing 100000,China
  • Received:2019-08-20 Revised:2019-10-23 Accepted:2019-10-24 Online:2019-10-31 Published:2020-02-10
  • Contact: Jiajin HUNAG
  • About author:DENG Kai, born in 1994, M. S. candidate. His research interests include recommendation system.
    QIN Jin, born in 1978, Ph. D., associate professor. His research interests include computational intelligence.
  • Supported by:
    the Science and Technology Program of Guizhou Province Science and Technology Department (Qiankehe Zhicheng[2019]2502)


邓凯1, 黄佳进2(), 秦进1   

  1. 1.贵州大学 计算机科学与技术学院,贵阳 550025
    2.北京工业大学 国际WIC研究院,北京 100000
  • 通讯作者: 黄佳进
  • 作者简介:邓凯(1994—),男,贵州毕节人,硕士研究生,主要研究方向:推荐系统
  • 基金资助:


The modeling of user-item interaction patterns is an important task for personalized recommendation. Many recommendation systems are based on the assumption that there is a linear relationship between users and items, and ignore the complexity and non-linearity of interaction between real and historical items, as a result, these systems cannot capture the complex decision-making process of users. Therefore, a more expressive top-N recommendation system’s item similarity factor model solution was combined with the multi-layer perceptron approach, to effectively model the higher-order relationships between items and capture more complex user decisions. The combination effect was verified on the three datasets of MovieLens, Foursquare and ratings_Digital_Music; and compared with the benchmark methods such as MLP (Multi-Layer Perception), Factored Item Similarity Model (FISM), DeepICF (Deep Item-based Collaborative Filtering) and ItemKNN (Item-based K-Nearest Neighbors), the results demonstrate that the proposed method has significant improvement in recommendation performance.

Key words: deep neural network, personalized recommendation, higher-order relationship, non-linearity, user decision making



关键词: 深度神经网络, 个性化推荐, 高阶关系, 非线性, 用户决策

CLC Number: