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Foundation Models for Recommender Systems: A Survey and New Perspectives
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Recently, Foundation Models (FMs), with their extensive knowledge bases and complex architectures, have offered unique opportunities within the realm of recommender systems (RSs). In this paper, we attempt to thoroughly examine FM-based recommendation systems (FM4RecSys). We start by reviewing the research background of FM4RecSys. Then, we provide a systematic taxonomy of existing FM4RecSys research works, which can be divided into four different parts including data characteristics, representation learning, model type, and downstream tasks. Within each part, we review the key recent research developments, outlining the representative models and discussing their characteristics. Moreover, we elaborate on the open problems and opportunities of FM4RecSys aiming to shed light on future research directions in this area. In conclusion, we recap our findings and discuss the emerging trends in this field.
Forward citations
Cited by 8 Pith papers
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RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation
A from-scratch model that tokenizes items into hierarchical codes and predicts next-item codes reaches higher average zero-shot AUC on 8 datasets than LLM recommenders up to 7B parameters.
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Beyond Negative Transfer: Disentangled Preference-Guided Diffusion for Cross-Domain Sequential Recommendation
A diffusion-based recommender for cross-domain sequential recommendation with disentangled preference guidance claims strong gains over prior baselines, but the reported numbers are internally inconsistent.
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Generative Representational Learning of Foundation Models for Recommendation
A single recommendation model with task-aware Mixture of Low-rank Experts and convergence-based sample scheduling beats baselines on a new 13-task benchmark.
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Beyond Item Order: Temporal Gap Tokenization for Generative Recommendation with Semantic IDs
Interleaving fixed log-scale gap tokens with semantic IDs, plus TA-FAMAE temporal regularization, consistently beats ReSID and other SID generative baselines on Amazon sequential recommendation.
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PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform
A single pretrained model over user activity sequences improves save rates in Pinterest's Home Feed and Related Items ranking when fine-tuned per application, while deduplication and quantization keep serving costs neutral.
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LIBER: Lifelong User Behavior Modeling Based on Large Language Models
LIBER partitions lifelong user behavior into fixed chunks, uses LLMs to summarize each chunk and detect interest shifts, and fuses these summaries to improve CTR prediction.
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Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach
MRFF, a federated sequential recommender with a group gating network and private user FFNs, improves CTR prediction over FedSASRec, FedHSTU, and FedLLaMA on three Kuai datasets.
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Large Language Model Enhanced Recommender Systems: A Survey
A survey organizing LLM-enhanced recommender systems into knowledge, interaction, and model enhancement, and tracing a shift from explicit text to implicit embeddings and fine-tuned open-source LLMs.
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