Large language models (LLMs) have demonstrated impressive capabilities i...
The research field of Information Retrieval (IR) has evolved significant...
Learning on Graphs has attracted immense attention due to its wide real-...
Elementary trapping sets (ETSs) are the main culprits for the performanc...
Link prediction attempts to predict whether an unseen edge exists based ...
Passage retrieval is a fundamental task in many information systems, suc...
Search engine plays a crucial role in satisfying users' diverse informat...
Recently, a new paradigm called Differentiable Search Index (DSI) has be...
The goal of unbiased learning to rank (ULTR) is to leverage implicit use...
Event extraction aims to recognize pre-defined event triggers and argume...
Recent studies show that graph neural networks (GNNs) are prevalent to m...
Large Language Models (LLMs) have demonstrated a remarkable ability to
g...
Conventional document retrieval techniques are mainly based on the
index...
In this study, an optimization model for offline scheduling policy of
lo...
Improving user retention with reinforcement learning (RL) has attracted
...
Identifying high-quality webpages is fundamental for real-world search
e...
Natural language understanding (NLU) models often rely on dataset biases...
Pre-trained language models have become a crucial part of ranking system...
The page presentation biases in the information retrieval system, especi...
Embedding-based retrieval (EBR) is a technique to use embeddings to repr...
Extracting query-document relevance from the sparse, biased clickthrough...
Unbiased Learning to Rank (ULTR) that learns to rank documents with bias...
Expanding an existing tourist photo from a partially captured scene to a...
The unbiased learning to rank (ULTR) problem has been greatly advanced b...
Self-supervised learning (especially contrastive learning) methods on
he...
Knowledge graphs (KGs) facilitate a wide variety of applications due to ...
Neural retrievers based on pre-trained language models (PLMs), such as
d...
Collaborative Filtering (CF) has emerged as fundamental paradigms for
pa...
Passage re-ranking is to obtain a permutation over the candidate passage...
Query understanding plays a key role in exploring users' search intents ...
Modeling user sequential behaviors has recently attracted increasing
att...
A well-informed recommendation framework could not only help users ident...
How to extract meaningful information in user historical behavior plays ...
Recently, sequential recommendation systems are important in solving the...
Social recommendation which aims to leverage social connections among us...
Despite the remarkable success deep models have achieved in Textual Matc...
Question generation (QG) is to generate natural and grammatical question...
Retrieval is a crucial stage in web search that identifies a small set o...
Post-click conversion, as a strong signal indicating the user preference...
As the heart of a search engine, the ranking system plays a crucial role...
Sequential decision-making under cost-sensitive tasks is prohibitively
d...
Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from...
Recommendation reason generation, aiming at showing the selling points o...
Topic drift is a common phenomenon in multi-turn dialogue. Therefore, an...
In this paper, we study collaborative filtering in an interactive settin...
Robust Reinforcement Learning aims to find the optimal policy with some
...
Current state-of-the-art neural dialogue models learn from human
convers...
Neural conversational models learn to generate responses by taking into
...
Current state-of-the-art neural dialogue systems are mainly data-driven ...
Neural conversation systems generate responses based on the
sequence-to-...