A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling

Applied Sciences

Cheng Wang¹  Yaxi Song¹  Zhiyong Zhao¹  Qing Li¹  Xinghao Zhang¹  Jibing Gong¹  Shuli Wang² 

[1] School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China;The Key Lab for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China;

关键词: deep neural network;    matrix factorization;    top-N recommendation;    implicit feedback information;    meta-path bias;   

DOI  :  10.3390/app11167418

来源: DOAJ

【 摘 要 】

To provide more accurate and stable recommendations, it is necessary to combine display information with implicit information and to dig out potential information. Existing methods only consider explicit feedback information or implicit feedback information unilaterally and ignore the potential information of explicit feedback information and implicit feedback information, which is also crucial to the accuracy of the recommendation system. However, the traditional Heterogeneous Information Networks (HIN) recommendation ignores the attribute information in the meta-path and the interaction between the user and the item and, instead, only considers the linear characteristics of the user-object often ignoring its non-linear characteristics. Aiming at the potential information acquisition problem from assorted feedback, we propose a new top-N recommendation method MFDNN for Heterogeneous Information Networks (HINs). First, we consider explicit and implicit feedback information to determine the potential preferences of users and the potential features of the product. Then, matrix factorization (MF) and a deep neural network (DNN) are fused to learn independent feature embeddings through MF and DNN, and fully considering the linear and non-linear characteristics of the user-object. MFDNN was tested on several real data sets, such as Movie-Lens, and compared with benchmark experiments. MFDNN significantly improved the hit ratio (HR) and normalized discounted cumulative gain (NDCG). Further research showed that the meta-path bias had an excellent effect on the gain of potential information mining and the fusion of explicit and implicit information in the accuracy and stability of user interest classification.

【 授权许可】

Unknown