The research field of Information Retrieval (IR) has evolved significantly, expanding beyond
traditional search to meet diverse user information needs. Recently, Large Language …

Online advertising, as a vast market, has gained significant attention in various platforms
ranging from search engines, third-party websites, social media, and mobile apps. The …

Recommending cold items in recommendation systems is a longstanding challenge due to
the inherent differences between warm items, which are recommended based on user …

Recommender systems are feedback loop systems, which often face bias problems such as
popularity bias, previous model bias and position bias. In this paper, we focus on solving the …

In an era dominated by information overload, effective recommender systems are essential
for managing the deluge of data across digital platforms. Multi-stage cascade ranking …

In practical recommendation scenarios, users often interact with items under multi-typed
behaviors (eg, click, add-to-cart, and purchase). Traditional collaborative filtering techniques …

Short video applications have attracted billions of users in recent years, fulfilling their various
needs with diverse content. Users usually watch short videos on many topics on mobile …

Multi-task learning (MTL) has been widely used in recommender systems, wherein
predicting each type of user feedback on items (eg, click, purchase) are treated as individual …

Graph-based recommender system has attracted widespread attention and produced a
series of research results. Because of the powerful high-order connection modeling …

Sequential recommendation aims at predicting the next item that the user may be interested
in given the historical interaction sequence. Typical neural models derive a single history …