Deep Attentive Multimodal Network Representation Learning for Social Media Images

Abstract

The analysis for social networks, such as the socially connected Internet of Things, has shown a deep influence of intelligent information processing technology on industrial systems for Smart Cities. The goal of social media representation learning is to learn dense, low-dimensional, and continuous representations for multimodal data within social networks, facilitating many real-world applications. Since social media images are usually accompanied by rich metadata (e.g., textual descriptions, tags, groups, and submitted users), simply modeling the image is not effective to learn the comprehensive information from social media images. In this work, we treat the image and its textual description as multimodal content, and transform other metainformation into the links between contents (such as two images marked by the same tag or submitted by the same user). Based on the multimodal content and social links, we propose a Deep Attentive Multimodal Graph Embedding model named DAMGE for more effective social image representation learning. We introduce both small- and large-scale datasets to conduct extensive experiments, of which the results confirm the superiority of the proposal on the tasks of social image classification and link prediction.

Student Performance Prediction via Attention-Based Multi-Layer Long-Short Term Memory

“…References [30] use a multi-layer Artificial Neural Network model to predict dropout. LSTM model is proved to be effective in many fields of artificial intelligence as shown in [31] [32] [33] [34] [35]. Some scholars apply multi-layer LSTM models [36] [37] to predict student performance.…”

Developing A Contextual Combinational Approach for Predictive Analysis of Users Mobile Phone Trajectory Data in LBSNs

“…This helps provide valuable recommendations to users who lack enough historical check-ins and is generally referred to as the cold-start problem. However, the employment of collaboration filtering (CF)-based methods complicates the processing of sequence data and capturing of dynamic user's preferences [2,6,11]. As a result, the ongoing challenges lie in the manner of integrating the information of different features to accurately model users' complex behavioral preferences and then recommending reliable POIs [13].…”

“…Thus, long-term dependencies can be captured by the hidden states of recurrent methods [4,16]. Many types of recurrent-based approaches have considered geographical and temporal factors to enhance the performance of POI recommendation algorithms [2,4,11,12,15]. Nonetheless, the present RNN-based POI recommendation methods face the alleviation of the cold-start problem [11].…”

“…Many types of recurrent-based approaches have considered geographical and temporal factors to enhance the performance of POI recommendation algorithms [2,4,11,12,15]. Nonetheless, the present RNN-based POI recommendation methods face the alleviation of the cold-start problem [11]. In this regard, one of the excellent choices is to incorporate RNN-based POI recommendation methods with the MF method to enjoy the benefits of each one [2].…”

“…In this regard, one of the excellent choices is to incorporate RNN-based POI recommendation methods with the MF method to enjoy the benefits of each one [2]. The user's historical check-in behaviors do not significantly pose any problems in predicting the next behavior; hence, it is necessary to take only the important information into serious consideration [1,11]. Therefore, the attention mechanism (AM) has been proposed to deal with this challenge.…”