DenseNet-based attentive plot-aware recommendation. (23rd November 2020)

• Record Type: Journal Article
• Authors: Wang, SuHua; Ma, ZhiQiang; Sun, XiaoXin; Yuan, Yue
• Is Part Of: International journal of high performance systems architecture. Volume 9:Number 2/3(2020)
• Page Start: 77
• Page End: 86
• Publication Date: 2020-11-23
• Subjects: DenseNet -- plot-aware -- attention -- rating prediction -- deep learning -- recommendation system -- social networks -- collaborative filtering -- cold start -- movie recommendation; Computer architecture -- Periodicals; Computer systems -- Periodicals; High performance computing -- Periodicals; 004.205
• Languages: English
• ISSNs: 1751-6528

Abstract

Since the ratings matrix is always sparse, auxiliary information has been proved very important in recommender systems. In this paper, we propose a DenseNet-based attentive plot-aware recommendation (DAPR) model, which combines attention mechanism and densely connected convolutional networks (i.e., DenseNet) to fully mine the semantic information in the movie plot text. This method effectively fuses rating information and text information for ratings prediction. Extensive experiments on three popular datasets demonstrate that our model performs better than other state-of-the-art approaches in common recommendation tasks.