The general aim of the recommender system is to provide personalized suggestions to
users, which is opposed to suggesting popular items. However, the normal training …

Recommender systems (RSs) aim at hel** users to effectively retrieve items of their
interests from a large catalogue. For a quite long time, researchers and practitioners have …

Recommendation models are usually trained on observational interaction data. However,
observational interaction data could result from users' conformity towards popular items …

As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …

Collaborative filtering (CF) models easily suffer from popularity bias, which makes
recommendation deviate from users' actual preferences. However, most current debiasing …

Recommendation is a prevalent and critical service in information systems. To provide
personalized suggestions to users, industry players embrace machine learning, more …

Collaborative Filtering (CF) models, despite their great success, suffer from severe
performance drops due to popularity distribution shifts, where these changes are ubiquitous …

How to effectively mitigate the bias of feedback in recommender systems is an important
research topic. In this paper, we first describe the generation process of the biased and …

Multimedia-based recommendation (MMRec) models typically rely on observed user-item
interactions and the multimodal content of items, such as visual images and textual …

Recommender Systems are becoming ubiquitous in many settings and take many forms,
from product recommendation in e-commerce stores, to query suggestions in search …