FARM: A Fairness-Aware Recommendation Method for High Visibility and Low Visibility Mobile APPs
The number of mobile applications(APPs) has increased dramatically with the development of mobile Internet. It becomes challenging for users to identify these APPs they are really interested in. Existing mobile APP recommendation methods focus on learning users' preference and recommending high...
Main Authors: | Qiliang Zhu, Qibo Sun, Zengxiang Li, Shangguang Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9134908/ |
Similar Items
-
Modelling of a Roulette Wheel Selection Operator in Genetic Algorithms Using Generalized Nets
by: Pencheva T., et al.
Published: (2009-12-01) -
A Multi-Side Fairness-Aware Recommendation System Based on a Pareto-Efficient Perspective
by: Qingyue DU, et al.
Published: (2022-01-01) -
Improving Cached Data Offloading Optimization Based on Enhanced Hybrid Ant Colony Genetic Algorithm
by: Mulki Indana Zulfa, et al.
Published: (2022-01-01) -
Rainfall Prediction in Tengger, Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm Method
by: Ida Wahyuni, et al.
Published: (2017-04-01) -
Recommendations for Mobile Apps Based on the HITS Algorithm Combined With Association Rules
by: Xiangliang Zhong, et al.
Published: (2019-01-01)