Explainable recommendation: A survey and new perspectives
Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …
recommendations but also intuitive explanations. The explanations may either be post-hoc …
Characterizing context-aware recommender systems: A systematic literature review
Context-aware recommender systems leverage the value of recommendations by exploiting
context information that affects user preferences and situations, with the goal of …
context information that affects user preferences and situations, with the goal of …
Bias and debias in recommender system: A survey and future directions
While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …
system (RS), most of the papers focus on inventing machine learning models to better fit …
What is semantic communication? A view on conveying meaning in the era of machine intelligence
In the 1940s, Claude Shannon developed the information theory focusing on quantifying the
maximum data rate that can be supported by a communication channel. Guided by this …
maximum data rate that can be supported by a communication channel. Guided by this …
Session-based social recommendation via dynamic graph attention networks
Online communities such as Facebook and Twitter are enormously popular and have
become an essential part of the daily life of many of their users. Through these platforms …
become an essential part of the daily life of many of their users. Through these platforms …
A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks
R Chen, Q Hua, YS Chang, B Wang, L Zhang… - IEEE …, 2018 - ieeexplore.ieee.org
In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
A deep graph neural network-based mechanism for social recommendations
Z Guo, H Wang - IEEE Transactions on Industrial Informatics, 2020 - ieeexplore.ieee.org
Nowadays, the issue of information overload is gradually gaining exposure in the Internet of
Things (IoT), calling for more research on recommender system in advance for industrial IoT …
Things (IoT), calling for more research on recommender system in advance for industrial IoT …
Deep learning-embedded social internet of things for ambiguity-aware social recommendations
Z Guo, K Yu, Y Li, G Srivastava… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
With the increasing demand of users for personalized social services, social
recommendation (SR) has been an important concern in academia. However, current …
recommendation (SR) has been an important concern in academia. However, current …
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility
Recommendation systems are ubiquitous and impact many domains; they have the potential
to influence product consumption, individuals' perceptions of the world, and life-altering …
to influence product consumption, individuals' perceptions of the world, and life-altering …
Social collaborative filtering by trust
Recommender systems are used to accurately and actively provide users with potentially
interesting information or services. Collaborative filtering is a widely adopted approach to …
interesting information or services. Collaborative filtering is a widely adopted approach to …