Open AccessTrust-Based Neural Collaborative Filtering
Author(s) -
Yejia Zeng,
Zehui Qu
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1229/1/012051
Subject(s) - collaborative filtering , computer science , artificial intelligence , machine learning , recommender system , artificial neural network , matrix decomposition , kernel (algebra) , perceptron , data mining , mathematics , eigenvalues and eigenvectors , physics , quantum mechanics , combinatorics
In order to maintain the strong representational learning of the deep neural network model to learn the interaction function between any users and items, combining the trust relationship between users as a local relationship to enhance the ability to supplement interactive data, to achieve a better recommendation effect. This paper proposes a Trust-based Neural Collaborative Filtering model (TNCF). Firstly, trust information and scoring information are merged through the Generalized Matrix Factorization model (GMF) to obtain recommendations based on trust friend preferences. Then, using the Multi-Layer Perceptron model (MLP), the nonlinear kernel is utilized to learn the interaction function from the data to obtain the recommendation based on the user’s personal taste. Finally, all the interaction results are aggregated for implicit prediction. Compared with the three different baselines on the FilmTrust and Epinions datasets, the experimental consequences reveal that the proposed model improves the recommendation and also performs well on sparse statistics.