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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Recommendation Based Heterogeneous Information Network and Neural Network Model


    Weitere Titelangaben:

    Lect.Notes Social.Inform.


    Beteiligte:
    Wu, Qihui (Herausgeber:in) / Zhao, Kanglian (Herausgeber:in) / Ding, Xiaojin (Herausgeber:in) / Zhao, Cong (Autor:in) / Wen, Yan (Autor:in) / Chen, Ming (Autor:in) / Chen, Geng (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    28.02.2021


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch