# Content-Based Collaborative Generation for Recommender Systems

arXiv: https://arxiv.org/abs/2403.18480 · HTML: https://mlsanity.com/paper/2403.18480/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2024.

## Built on by (most cited first, 16 of 16)

- [Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations](https://mlsanity.com/paper/2503.02453/) (2025)
- [ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation](https://mlsanity.com/paper/2502.13581/) (2025)
- [A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms](https://mlsanity.com/paper/2504.16420/) (2025)
- [MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation](https://mlsanity.com/paper/2508.15281/) (2025)
- [Enhancing Item Tokenization for Generative Recommendation through Self-Improvement](https://mlsanity.com/paper/2412.17171/) (2024)
- [Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey](https://mlsanity.com/paper/2507.22920/) (2025)
- [Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval](https://mlsanity.com/paper/2407.21488/) (2024)
- [AMEM4Rec: Leveraging Cross-User Similarity for Memory Evolution in Agentic LLM Recommenders](https://mlsanity.com/paper/2602.08837/) (2026)
- [Generative Recommendation for Large-Scale Advertising](https://mlsanity.com/paper/2602.22732/) (2026)
- [GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks](https://mlsanity.com/paper/2506.16114/) (2025)
- [Purely Semantic Indexing for LLM-based Generative Recommendation and Retrieval](https://mlsanity.com/paper/2509.16446/) (2025)
- [Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs](https://mlsanity.com/paper/2509.02017/) (2025)
- [RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation](https://mlsanity.com/paper/2509.03131/) (2025)
- [Reconstructing Training Data from Adapter-based Federated Large Language Models](https://mlsanity.com/paper/2601.17533/) (2026)
- [CoNRec: Context-Discerning Negative Recommendation with LLMs](https://mlsanity.com/paper/2601.15721/) (2026)
- [ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation](https://mlsanity.com/paper/2604.22169/) (2026)

## Abstract (arXiv)

Generative models have emerged as a promising utility to enhance recommender systems. It is essential to model both item content and user-item collaborative interactions in a unified generative framework for better recommendation. Although some existing large language model (LLM)-based methods contribute to fusing content information and collaborative signals, they fundamentally rely on textual language generation, which is not fully aligned with the recommendation task. How to integrate content knowledge and collaborative interaction signals in a generative framework tailored for item recommendation is still an open research challenge. In this paper, we propose content-based collaborative generation for recommender systems, namely ColaRec. ColaRec is a sequence-to-sequence framework which is tailored for directly generating the recommended item identifier. Precisely, the input sequence comprises data pertaining to the user's interacted items, and the output sequence represents the generative identifier (GID) for the suggested item. To model collaborative signals, the GIDs are constructed from a pretrained collaborative filtering model, and the user is represented as the content aggregation of interacted items. To this end, ColaRec captures both collaborative signals and content information in a unified framework. Then an item indexing task is proposed to conduct the alignment between the content-based semantic space and the interaction-based collaborative space. Besides, a contrastive loss is further introduced to ensure that items with similar collaborative GIDs have similar content representations. To verify the effectiveness of ColaRec, we conduct experiments on four benchmark datasets. Empirical results demonstrate the superior performance of ColaRec.