# IntSR: An Integrated Generative Framework for Search and Recommendation

arXiv: https://arxiv.org/abs/2509.21179 · HTML: https://mlsanity.com/paper/2509.21179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 24 of 24)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation](https://mlsanity.com/paper/2108.12409/) (2021)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Recommender Systems with Generative Retrieval](https://mlsanity.com/paper/2305.05065/) (2023)
- [Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations](https://mlsanity.com/paper/2402.17152/) (2024)
- [Filter-enhanced MLP is All You Need for Sequential Recommendation](https://mlsanity.com/paper/2202.13556/) (2022)
- [Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation](https://mlsanity.com/paper/2207.05584/) (2022)
- [OneSearch: A Preliminary Exploration of the Unified End-to-End Generative Framework for E-commerce Search](https://mlsanity.com/paper/2509.03236/) (2025)
- [Word2Vec applied to Recommendation: Hyperparameters Matter](https://mlsanity.com/paper/1804.04212/) (2018)
- [When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation](https://mlsanity.com/paper/2305.10822/) (2023)
- [A Transformer-based Embedding Model for Personalized Product Search](https://mlsanity.com/paper/2005.08936/) (2020)
- [A Zero Attention Model for Personalized Product Search](https://mlsanity.com/paper/1908.11322/) (2019)
- [UniSAR: Modeling User Transition Behaviors between Search and Recommendation](https://mlsanity.com/paper/2404.09520/) (2024)
- [USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence](https://mlsanity.com/paper/2109.15012/) (2021)
- [Adaptive Hardness Negative Sampling for Collaborative Filtering](https://mlsanity.com/paper/2401.05191/) (2024)
- [Joint Modeling and Optimization of Search and Recommendation](https://mlsanity.com/paper/1807.05631/) (2018)
- [UnifiedSSR: A Unified Framework of Sequential Search and Recommendation](https://mlsanity.com/paper/2310.13921/) (2023)
- [Breaking the Curse of Quality Saturation with User-Centric Ranking](https://mlsanity.com/paper/2305.15333/) (2023)
- [DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking](https://mlsanity.com/paper/2404.00243/) (2024)

## Abstract (arXiv)

Generative recommendation has emerged as a promising paradigm, demonstrating remarkable results in both academic benchmarks and industrial applications. However, existing systems predominantly focus on unifying retrieval and ranking while neglecting the integration of search and recommendation (S&R) tasks. What makes search and recommendation different is how queries are formed: search uses explicit user requests, while recommendation relies on implicit user interests. As for retrieval versus ranking, the distinction comes down to whether the queries are the target items themselves. Recognizing the query as central element, we propose IntSR, an integrated generative framework for S&R. IntSR integrates these disparate tasks using distinct query modalities. It also addresses the increased computational complexity associated with integrated S&R behaviors and the erroneous pattern learning introduced by a dynamically changing corpus. IntSR has been successfully deployed across various scenarios in Amap, leading to substantial improvements in digital asset's GMV(+9.34%), POI recommendation's CTR(+2.76%), and travel mode suggestion's ACC(+7.04%).