# TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

arXiv: https://arxiv.org/abs/2206.06190 · HTML: https://mlsanity.com/paper/2206.06190/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2022.

## Built on by (most cited first, 27 of 27)

- [Recommender Systems with Generative Retrieval](https://mlsanity.com/paper/2305.05065/) (2023)
- [Recommender Systems in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2307.02046/) (2023)
- [How Can Recommender Systems Benefit from Large Language Models: A Survey](https://mlsanity.com/paper/2306.05817/) (2023)
- [Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders](https://mlsanity.com/paper/2210.12316/) (2022)
- [ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation](https://mlsanity.com/paper/2308.11131/) (2023)
- [Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations](https://mlsanity.com/paper/2306.08121/) (2023)
- [VIP5: Towards Multimodal Foundation Models for Recommendation](https://mlsanity.com/paper/2305.14302/) (2023)
- [Multimodal Recommender Systems: A Survey](https://mlsanity.com/paper/2302.03883/) (2023)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation](https://mlsanity.com/paper/2308.11175/) (2023)
- [IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT](https://mlsanity.com/paper/2404.02059/) (2024)
- [Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation](https://mlsanity.com/paper/2408.03533/) (2024)
- [Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation](https://mlsanity.com/paper/2411.02992/) (2024)
- [Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling](https://mlsanity.com/paper/2309.10435/) (2023)
- [Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation Models](https://mlsanity.com/paper/2410.17276/) (2024)
- [Large Language Model as Universal Retriever in Industrial-Scale Recommender System](https://mlsanity.com/paper/2502.03041/) (2025)
- [Large Language Models Make Sample-Efficient Recommender Systems](https://mlsanity.com/paper/2406.02368/) (2024)
- [ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation](https://mlsanity.com/paper/2606.28357/) (2026)
- [COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation](https://mlsanity.com/paper/2306.05001/) (2023)
- [BiVRec: Bidirectional View-based Multimodal Sequential Recommendation](https://mlsanity.com/paper/2402.17334/) (2024)
- [Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendation](https://mlsanity.com/paper/2510.05125/) (2025)
- [Transferable Sequential Recommendation via Vector Quantized Meta Learning](https://mlsanity.com/paper/2411.01785/) (2024)
- [VisualLens: Personalization through Task-Agnostic Visual History](https://mlsanity.com/paper/2411.16034/) (2024)
- [Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation](https://mlsanity.com/paper/2501.13344/) (2025)
- [Bridging Domain Gaps between Pretrained Multimodal Models and Recommendations](https://mlsanity.com/paper/2502.15542/) (2025)
- [Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations](https://mlsanity.com/paper/2507.21274/) (2025)
- [Cross-household Transfer Learning Approach with LSTM-based Demand Forecasting](https://mlsanity.com/paper/2602.14267/) (2026)

## Abstract (arXiv)

Learning large-scale pre-trained models on broad-ranging data and then transfer to a wide range of target tasks has become the de facto paradigm in many machine learning (ML) communities. Such big models are not only strong performers in practice but also offer a promising way to break out of the task-specific modeling restrictions, thereby enabling task-agnostic and unified ML systems. However, such a popular paradigm is mainly unexplored by the recommender systems (RS) community. A critical issue is that standard recommendation models are primarily built on categorical identity features. That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms. To pursue the transferable recommendations, we propose studying pre-trained RS models in a novel scenario where a user's interaction feedback involves a mixture-of-modality (MoM) items, e.g., text and images. We then present TransRec, a very simple modification made on the popular ID-based RS framework. TransRec learns directly from the raw features of the MoM items in an end-to-end training manner and thus enables effective transfer learning under various scenarios without relying on overlapped users or items. We empirically study the transferring ability of TransRec across four different real-world recommendation settings. Besides, we look at its effects by scaling source and target data size. Our results suggest that learning neural recommendation models from MoM feedback provides a promising way to realize universal RS.