Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

Large language models (LLMs) have demonstrated remarkable capabilities across various
research domains, including the field of Information Retrieval (IR). However, the responses …

Deep Recommender Systems (DRS) are increasingly dependent on a large number of
feature fields for more precise recommendations. Effective feature selection methods are …

Worldwide geolocalization aims to locate the precise location at the coordinate level of
photos taken anywhere on the Earth. It is very challenging due to 1) the difficulty of capturing …

Search engines are crucial as they provide an efficient and easy way to access vast
amounts of information on the internet for diverse information needs. User queries, even with …

Large language models (LLMs) have been used to generate query expansions augmenting
original queries for improving information search. Recent studies also explore providing …

The lack of training data gives rise to the system cold-start problem in recommendation
systems, making them struggle to provide effective recommendations. To address this …

Evaluating retrieval-augmented generation (RAG) systems remains challenging, particularly
for open-ended questions that lack definitive answers and require coverage of multiple sub …

User simulators can rapidly generate a large volume of timely user behavior data, providing
a testing platform for reinforcement learning-based recommender systems, thus accelerating …

Prompt engineering can significantly improve the performance of large language models
(LLMs), with automated prompt optimization (APO) gaining significant attention due to the …