Human-Centric Intelligent Systems
Volume 1, Issue 1-2, June 2021
1. Human-Centric Intelligent Systems: A Welcome Note from Editor(s)-in-Chief
Tianrui Li, Guandong Xu
Pages: 1 - 2
2. Deep Visual Analytics (DVA): Applications, Challenges and Future Directions
Md Rafiqul Islam, Shanjita Akter, Md Rakybuzzaman Ratan, Abu Raihan M. Kamal, Guandong Xu
Pages: 3 - 17
Visual interactive system (VIS) has been received significant attention for solving various complex problems. However, designing and implementing a novel VIS with the large scale of data is a challenging task. While existing studies have applied various visual analytics (VA) to analyze and visualize...
3. An Empirical Study of Learning Based Happiness Prediction Approaches
Miao Kong, Lin Li, Renwei Wu, Xiaohui Tao
Pages: 18 - 24
In today’s society, happiness has attracted more and more attentions from researchers. It is interesting to study happiness from the perspective of data mining. In psychology domain, the application of data mining gradually becomes widespread and popular, which works from a novel data-driven viewpoint....
4. Interactive Attention-Based Convolutional GRU for Aspect Level Sentiment Analysis
Lisha Chen, Tianrui Li, Huaishao Luo, Chengfeng Yin
Pages: 25 - 31
Aspect level sentiment analysis aims at identifying sentiment polarity towards specific aspect terms in a given sentence. Most methods based on deep learning integrate Recurrent Neural Network (RNN) and its variants with the attention mechanism to model the influence of different context words on sentiment...
5. Semantic Knowledge Discovery for User Profiling for Location-Based Recommender Systems
Xiaohui Tao, Nischal Sharma, Patrick Delaney, Aimin Hu
Pages: 32 - 42
This paper introduces a purposed Location-based Recommender System (LBRS) that combines sentiment analysis and topic modelling techniques to improve user profiling for enhancing recommendations of Points of Interest (POIs). Using additional feature extraction, we built user profiles from a Foursquare...
6. Context-Based User Typicality Collaborative Filtering Recommendation
Jinzhen Zhang, Qinghua Zhang, Zhihua Ai, Xintai Li
Pages: 43 - 53
Since contextual information significantly affecting users’ decisions, it has attracted widespread attention. User typicality indicates the preference of user for different item types, which could reflect the preference of user at a higher abstraction level than the items rated by user, and can alleviate...