With the advent of the Internet era, the recommendation system has developed rapidly. Heterogeneous information networks representation learning is widely used in recommendation systems due to its advantages in complex information modeling. Although the performance of the recommendation system has been improved, there are still two shortcomings: 1. There is a lot of noise data in the instances of the meta-path generated by random walks of meta-paths, which will reduce the performance of the recommendation system. 2. Traditional recommendation algorithms fail to make full use of the relevant meta-path information in heterogeneous information networks, which makes the recommendation results lack of interpretability. To solve these problems, we propose a recommendation system based on heterogeneous network representation learning and neural network model. Firstly, use the matrix factorization and the similarity calculation to select the meta-path instances with good quality as the pre-training vectors of the recommendation system. Then, we combine LSTM with Attention mechanism to learn the user, item and meta-path embeddings, and use MLP to make prediction after fusion to jointly improve the recommendation effect. We conducted experiments on Movielens datasets to evaluate the performance of our proposed recommendation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Recommendation Based Heterogeneous Information Network and Neural Network Model


    Additional title:

    Lect.Notes Social.Inform.


    Contributors:
    Wu, Qihui (editor) / Zhao, Kanglian (editor) / Ding, Xiaojin (editor) / Zhao, Cong (author) / Wen, Yan (author) / Chen, Ming (author) / Chen, Geng (author)

    Conference:

    International Conference on Wireless and Satellite Systems ; 2020 ; Nanjing, China September 17, 2020 - September 18, 2020



    Publication date :

    2021-02-28


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English