Human-Centric Intelligent Systems

Volume 1, Issue 1-2, June 2021
Review Article

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...
Research Article

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....
Research Article

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...
Research Article

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...
Research Article

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...