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

Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs)
have been effectively utilized as recommenders, achieving impressive performance …

Utilizing powerful Large Language Models (LLMs) for generative recommendation has
attracted much attention. Nevertheless, a crucial challenge is transforming recommendation …

Adapting Large Language Models (LLMs) for recommendation requires careful
consideration of the decoding process, given the inherent differences between generating …

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 …

With the thriving of the pre-trained language model (PLM) widely verified in various NLP
tasks, pioneer efforts attempt to explore the possible cooperation of the general textual …

Abstract Adapting Large Language Models (LLMs) for recommendation requires careful
consideration of the decoding process, given the inherent differences between generating …

Agents powered by large language models have shown remarkable reasoning and
execution capabilities, attracting researchers to explore their potential in the …

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 …

Frequently updating Large Language Model (LLM)-based recommender systems to adapt to
new user interests--as done for traditional ones--is impractical due to high training costs …