How does generative retrieval scale to millions of passages?
Popularized by the Differentiable Search Index, the emerging paradigm of generative
retrieval re-frames the classic information retrieval problem into a sequence-to-sequence …
retrieval re-frames the classic information retrieval problem into a sequence-to-sequence …
Scalable and effective generative information retrieval
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 …
indexes by representing each document as a sequence of document ID tokens. These …
Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning
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 …
be effective and efficient. Recently, generative retrieval has become an emerging paradigm …
Text-like Encoding of Collaborative Information in Large Language Models for Recommendation
When adapting Large Language Models for Recommendation (LLMRec), it is crucial to
integrate collaborative information. Existing methods achieve this by learning collaborative …
integrate collaborative information. Existing methods achieve this by learning collaborative …
Prompt Tuning as User Inherent Profile Inference Machine
Large Language Models (LLMs) have exhibited significant promise in recommender
systems by empowering user profiles with their extensive world knowledge and superior …
systems by empowering user profiles with their extensive world knowledge and superior …
Gen-IR@ SIGIR 2024: The Second Workshop on Generative Information Retrieval
Generative information retrieval (Gen-IR) is a fast-growing interdisciplinary research area
that investigates how to leverage advances in generative Artificial Intelligence (AI) to …
that investigates how to leverage advances in generative Artificial Intelligence (AI) to …
Preference Distillation for Personalized Generative Recommendation
Recently, researchers have investigated the capabilities of Large Language Models (LLMs)
for generative recommender systems. Existing LLM-based recommender models are trained …
for generative recommender systems. Existing LLM-based recommender models are trained …
CALRec: Contrastive Alignment of Generative LLMs For Sequential Recommendation
Traditional recommender systems such as matrix factorization methods rely on learning a
shared dense embedding space to represent both items and user preferences. Sequence …
shared dense embedding space to represent both items and user preferences. Sequence …
LiNR: Model Based Neural Retrieval on GPUs at LinkedIn
F Borisyuk, Q Song, M Zhou, G Parameswaran… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …
supports a billion-sized index on GPU models. We discuss our experiences and challenges …
Generative Retrieval via Term Set Generation
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 …
retrieval paradigms. It assigns each document a unique identifier, known as the DocID, and …