Information Retrieval (IR) systems are crucial tools for users to access information, widely
applied in scenarios like search engines, question answering, and recommendation …

Large language models (LLMs) augmented with external data have demonstrated
remarkable capabilities in completing real-world tasks. Techniques for integrating external …

With the information explosion on the Web, search and recommendation are foundational
infrastructures to satisfying users' information needs. As the two sides of the same coin, both …

Generative retrieval has recently emerged as a promising approach to sequential
recommendation, framing candidate item retrieval as an autoregressive sequence …

Generative retrieval, which has demonstrated effectiveness in text-to-text retrieval, utilizes a
sequence-to-sequence model to directly generate candidate identifiers based on natural …

Recently, generative recommendation has emerged as a promising new paradigm that
directly generates item identifiers for recommendation. However, a key challenge lies in how …

Current text-video retrieval methods mainly rely on cross-modal matching between queries
and videos to calculate their similarity scores, which are then sorted to obtain retrieval …

Graph neural network (GNN) has been a powerful approach in collaborative filtering (CF)
due to its ability to model high-order user-item relationships. Recently, to alleviate the data …

Generative models have emerged as a promising utility to enhance recommender systems.
It is essential to model both item content and user-item collaborative interactions in a unified …