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 …
Trustworthy recommender systems
Recommender systems (RSs) aim at helping users to effectively retrieve items of their
interests from a large catalogue. For a quite long time, researchers and practitioners have …
interests from a large catalogue. For a quite long time, researchers and practitioners have …
Maximizing marginal fairness for dynamic learning to rank
Rankings, especially those in search and recommendation systems, often determine how
people access information and how information is exposed to people. Therefore, how to …
people access information and how information is exposed to people. Therefore, how to …
A large scale search dataset for unbiased learning to rank
The unbiased learning to rank (ULTR) problem has been greatly advanced by recent deep
learning techniques and well-designed debias algorithms. However, promising results on …
learning techniques and well-designed debias algorithms. However, promising results on …
Measuring and improving user experience through artificial intelligence-aided design
B Yang, L Wei, Z Pu - Frontiers in Psychology, 2020 - frontiersin.org
This paper aims to propose a methodology for measuring user experience (UX) by using
artificial intelligence-aided design (AIAD) technology in mobile application design. Unlike …
artificial intelligence-aided design (AIAD) technology in mobile application design. Unlike …
Cross-positional attention for debiasing clicks
A well-known challenge in leveraging implicit user feedback like clicks to improve real-world
search services and recommender systems is its inherent bias. Most existing click models …
search services and recommender systems is its inherent bias. Most existing click models …
Adapting interactional observation embedding for counterfactual learning to rank
Counterfactual Learning to Rank (CLTR) becomes an attractive research topic due to its
capability of training ranker with click logs. However, CLTR inherently suffers from a large …
capability of training ranker with click logs. However, CLTR inherently suffers from a large …
Deep landscape forecasting in multi-slot real-time bidding
Real-Time Bidding (RTB) has shown remarkable success in display advertising and has
been employed in other advertising scenarios, eg, sponsored search advertising with …
been employed in other advertising scenarios, eg, sponsored search advertising with …
Deep position-wise interaction network for ctr prediction
Click-through rate (CTR) prediction plays an important role in online advertising and
recommender systems. In practice, the training of CTR models depends on click data which …
recommender systems. In practice, the training of CTR models depends on click data which …
Multi-scale user behavior network for entire space multi-task learning
Modelling the user's multiple behaviors is an essential part of modern e-commerce, whose
widely adopted application is to jointly optimize click-through rate (CTR) and conversion rate …
widely adopted application is to jointly optimize click-through rate (CTR) and conversion rate …