Popularity prediction for marketer-generated content: A text-guided attention neural network for multi-modal feature fusion. Issue 4 (July 2022)

• Record Type: Journal Article
• Authors: Qian, Yang; Xu, Wang; Liu, Xiao; Ling, Haifeng; Jiang, Yuanchun; Chai, Yidong; Liu, Yezheng
• Is Part Of: Information processing & management. Volume 59:Issue 4(2022)
• Publication Date: 2022-07
• Subjects: Popularity prediction -- Marketer-generated content -- Text-guided attention neural network -- Multi-modal data; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.102984
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: The popularity of marketer-generated content is predicted. A text-guided attention neural network model is proposed. Topic feature and image feature are explored in the proposed model. Performance of our model is tested by experiments on two real-world datasets. Abstract: In this paper, we focus on the popularity prediction for marketer-generated content (MGC), which has not been investigated by current studies. To address this problem, we propose a novel deep learning approach, namely the text-guided attention neural network (TGANN) model, to make full use of heterogeneous and multi-modal data related to MGCs (e.g., text descriptions and images). In the proposed model, we first design a filter-based topic model to filter out the noise words and extract topic features from textual descriptions. To lessen the influence of irrelevant information in images, we then propose a text-guided attention mechanism to use the text's topic features to guide the image region representations. Lastly, to determine the contribution of each topic and each image, the TGANN model introduces the attention computation for each visual modality and textual modality. Our experiments are conducted on two real-world datasets of MGC. The quantitative results show that the proposed model outperforms several state-of-the-art methods. The qualitative experiments demonstrate that our model can accurately capture topic attentions, image attentions, and image region attentions. The proposed model provides important practical implications for marketers and online platforms, e.g., estimating the success of online advertising campaigns, creating more attractive marketing contents, and improving recommendation systems.