Federated recommender system (FRS), which enables many local devices to train a shared
model jointly without transmitting local raw data, has become a prevalent recommendation …

As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects
user experience and satisfaction by rearranging the input ranking lists, and thereby plays a …

The Relevance Feedback (RF) process relies on accurate and real-time relevance
estimation of feedback documents to improve retrieval performance. Since collecting explicit …

Ranking interfaces are everywhere in online platforms. There is thus an ever growing
interest in their Off-Policy Evaluation (OPE), aiming towards an accurate performance …

Most click models focus on user behaviors towards a single list. However, with the
development of user interface (UI) design, the layout of displayed items on a result page …

This paper studies unsupervised domain adaptive hashing, which aims to transfer a hashing
model from a label-rich source domain to a label-scarce target domain. Current state-of-the …

Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on items,
plays a key fundamental role in sponsored search. E-commerce platforms display organic …

Click behavior is a typical user behavior in the web search. How to capture and model users'
click behavior has always been a common research topic. However, there are few review …

Modern search engine result pages (SERPs) become increasingly complex with
heterogeneous information aggregated from various sources. In many cases, these SERPs …

Counterfactual learning to rank (CLTR) has attracted extensive attention in the IR community
for its ability to leverage massive logged user interaction data to train ranking models. While …