27 citations · 36 across the 5 of their papers we have counts for
7 papers · 1 filter
PriCoRec: A Privacy-Aware Cloud-Device Collaborative Framework for Ad Recommendation under Feature Constraints
Dairui Liu, Zhongyi Lu, Jitao Lu +7
Privacy regulations increasingly restrict cloud processing of sensitive user data (e.g., age, gender), hindering traditional cloud-only recommendation models. To mitigate this chal…
RAMP: Robust Ad Recommendation Under Limited Personalized-Feature Availability via Masking and Alignment Pathways
Dairui Liu, Zhongyi Lu, Roger Zhe Li +11
Click-through rate (CTR) and conversion rate (CVR) prediction are fundamental tasks in online advertising, aiming to estimate the likelihood of user interactions based on various f…
From Raw IDs to Semantic Planning: How Recommender Systems Utilize Information at Scale
Changhong Jin, Shiqiu Yang, Roger Zhe Li +8
The evolution of recommender systems can be explored by asking how they utilize information at scale. Throughout most of the historical period under consideration during the past t…
Metric Optimization and Mainstream Bias Mitigation in Recommender Systems
Roger Zhe Li
The first part of this thesis focuses on maximizing the overall recommendation accuracy. This accuracy is usually evaluated with some user-oriented metric tailored to the recommend…
Mitigating Mainstream Bias in Recommendation via Cost-sensitive Learning
Roger Zhe Li, Julián Urbano, Alan Hanjalic
Mainstream bias, where some users receive poor recommendations because their preferences are uncommon or simply because they are less active, is an important aspect to consider reg…
New Insights into Metric Optimization for Ranking-based Recommendation
Roger Zhe Li, Julián Urbano, Alan Hanjalic
Direct optimization of IR metrics has often been adopted as an approach to devise and develop ranking-based recommender systems. Most methods following this approach aim at optimiz…
Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users
Roger Zhe Li, Julián Urbano, Alan Hanjalic
In a collaborative-filtering recommendation scenario, biases in the data will likely propagate in the learned recommendations. In this paper we focus on the so-called mainstream bi…