This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …

Transformer models have achieved remarkable success in sequential recommender
systems (SRSs). However, computing the attention matrix in traditional dot-product attention …

Deep learning-based recommender systems (DLRSs) often have embedding layers, which
are utilized to lessen the dimension of categorical variables (eg, user/item identifiers) and …

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous
interests in utilizing RL for online advertising in recommendation platforms (eg, e-commerce …

Practical large-scale recommender systems usually contain thousands of feature fields from
users, items, contextual information, and their interactions. Most of them empirically allocate …

Feature selection plays an impactful role in deep recommender systems, which selects a
subset of the most predictive features, so as to boost the recommendation performance and …

Conversational Information Retrieval (CIR) is an emerging field of Information Retrieval (IR)
at the intersection of interactive IR and dialogue systems for open domain information …

Online recommendation and advertising are two major income channels for online
recommendation platforms (eg e-commerce and news feed site). However, most platforms …

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous
interests in developing RL-based recommender systems. In practical recommendation …

We consider solving sequential decision-making problems in the scenario where the agent
has access to two supervision sources: $\textit {reward signal} $ and a $\textit {teacher} …