Popularized by the Differentiable Search Index, the emerging paradigm of generative
retrieval re-frames the classic information retrieval problem into a sequence-to-sequence …

Recent research has shown that transformer networks can be used as differentiable search
indexes by representing each document as a sequence of document ID tokens. These …

The retrieval phase is a vital component in recommendation systems, requiring the model to
be effective and efficient. Recently, generative retrieval has become an emerging paradigm …

When adapting Large Language Models for Recommendation (LLMRec), it is crucial to
integrate collaborative information. Existing methods achieve this by learning collaborative …

Large Language Models (LLMs) have exhibited significant promise in recommender
systems by empowering user profiles with their extensive world knowledge and superior …

Generative information retrieval (Gen-IR) is a fast-growing interdisciplinary research area
that investigates how to leverage advances in generative Artificial Intelligence (AI) to …

Recently, researchers have investigated the capabilities of Large Language Models (LLMs)
for generative recommender systems. Existing LLM-based recommender models are trained …

Traditional recommender systems such as matrix factorization methods rely on learning a
shared dense embedding space to represent both items and user preferences. Sequence …

This paper introduces LiNR, LinkedIn's large-scale, GPU-based retrieval system. LiNR
supports a billion-sized index on GPU models. We discuss our experiences and challenges …

Recently, generative retrieval has emerged as a promising alternative to the traditional
retrieval paradigms. It assigns each document a unique identifier, known as the DocID, and …