Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

Recommender system plays a vital role in various online services. However, its insulated
nature of training and deploying separately within a specific closed domain limits its access …

Reinforcement Learning (RL)-based recommender systems (RSs) have garnered
considerable attention due to their ability to learn optimal recommendation policies and …

Feed recommendation systems, which recommend a sequence of items for users to browse
and interact with, have gained significant popularity in practical applications. In feed …

In learning-to-rank for information retrieval, a ranking model is automatically learned from
the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal …

Ranking problems, also known as preference learning problems, define a widely spread
class of statistical learning problems with many applications, including fraud detection …

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 …

Recently, short video platforms have achieved rapid user growth by recommending
interesting content to users. The objective of the recommendation is to optimize user …

Matching is the key problem in both search and recommendation, that is to measure the
relevance of a document to a query or the interest of a user on an item. Previously, machine …

Personalized recommender systems fulfill the daily demands of customers and boost online
businesses. The goal is to learn a policy that can generate a list of items that matches the …