Causal inference in recommender systems: A survey and future directions
Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …
recommender systems extract user preferences based on the correlation in data, such as …
Representation learning with large language models for recommendation
Recommender systems have seen significant advancements with the influence of deep
learning and graph neural networks, particularly in capturing complex user-item …
learning and graph neural networks, particularly in capturing complex user-item …
Incorporating bias-aware margins into contrastive loss for collaborative filtering
Collaborative filtering (CF) models easily suffer from popularity bias, which makes
recommendation deviate from users' actual preferences. However, most current debiasing …
recommendation deviate from users' actual preferences. However, most current debiasing …
Fairness in recommendation: A survey
As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision making. The satisfaction of users and …
playing an important role on assisting human decision making. The satisfaction of users and …
Causal representation learning for out-of-distribution recommendation
Modern recommender systems learn user representations from historical interactions, which
suffer from the problem of user feature shifts, such as an income increase. Historical …
suffer from the problem of user feature shifts, such as an income increase. Historical …
[HTML][HTML] A survey on fairness-aware recommender systems
As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …
life by providing personalized suggestions and facilitating people in decision-making, which …