mlsanityData to 7 Oct 2026

Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging

arXiv 2309.01026 ↗First posted 2 Sep 2023cs.AI cs.CL cs.IRcited by 4

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Abstract (arXiv)

We present a method for zero-shot recommendation of multimodal non-stationary content that leverages recent advancements in the field of generative AI. We propose rendering inputs of different modalities as textual descriptions and to utilize pre-trained LLMs to obtain their numerical representations by computing semantic embeddings. Once unified representations of all content items are obtained, the recommendation can be performed by computing an appropriate similarity metric between them without any additional learning. We demonstrate our approach on a synthetic multimodal nudging environment, where the inputs consist of tabular, textual, and visual data.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 75 references that are in the map. First posted on arXiv 2 Sep 2023.

Builds on

19 of 75 references.

References · 19

  1. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  2. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  3. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  4. 2020A Review of Generalized Zero-Shot Learning Methodscited by 159
  5. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  6. 2019RecSim: A Configurable Simulation Platform for Recommender Systemscited by 91
  7. 2018RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertisingcited by 65
  8. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
  9. 2021Zero-Shot Recommender Systemscited by 45
  10. 2023Multimodal Recommender Systems: A Surveycited by 36
  11. 2019From Zero-Shot Learning to Cold-Start Recommendationcited by 29
  12. 2015The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Reviewcited by 27
7 more
  1. 2023A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directionscited by 17
  2. 2020Digital Nudging with Recommender Systems: Survey and Future Directionscited by 16
  3. 2018Robust Actor-Critic Contextual Bandit for Mobile Health (mHealth) Interventionscited by 9
  4. 2018A clustering-based reinforcement learning approach for tailored personalization of e-Health interventionscited by 8
  5. 2021MM-Rec: Multimodal News Recommendationcited by 6
  6. 2021On the Unreasonable Efficiency of State Space Clustering in Personalization Taskscited by 3
  7. 2022Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasetscited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  2. 2024An Empirical Categorization of Prompting Techniques for Large Language Models: A Practitioner's Guidecited by 7
  3. 2023Large Scale Foundation Models for Intelligent Manufacturing Applications: A Surveycited by 6
  4. 2025Driver Assistant: Persuading Drivers to Adjust Secondary Tasks Using Large Language Modelscited by 0