Since its inception, the field of unbiased learning to rank (ULTR) has remained very active
and has seen several impactful advancements in recent years. This tutorial provides both an …

In this technical survey, the latest advancements in the field of recommender systems are
comprehensively summarized. The objective of this study is to provide an overview of the …

Sequential recommendation holds the promise of understanding user preference by
capturing successive behavior correlations. Existing research focus on designing different …

Watch-time prediction remains to be a key factor in reinforcing user engagement via video
recommendations. It has become increasingly important given the ever-growing popularity …

Biases and de-biasing in recommender systems (RS) have become a research hotspot
recently. This paper reveals an unexplored type of bias, ie, sentiment bias. Through an …

With the prosperity of recommender systems, the biases existing in user behaviors, which
may lead to inconsistency between user preference and behavior records, have attracted …

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 …

Graph convolutional networks (GCNs) have become prevalent in recommender system (RS)
due to their superiority in modeling collaborative patterns. Although improving the overall …

Recommender systems suffer from confounding biases when there exist confounders
affecting both item features and user feedback (eg like or not). Existing causal …

Visually-aware recommendation on E-commerce platforms aims to leverage visual
information of items to predict a user's preference for these items in addition to the historical …