Hugging Face Trending Papers June 17, 2026

JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling

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Sequence modeling has become increasingly popular in recommendation and ranking algorithms, owing to its capacity to model users' historical behaviors and infer user intentions. Despite its theoretical simplicity, the practical deployment of a sequence model in production is non-trivial due to complexity of the sequence and sparse labels.

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arXiv Machine Learning

Jun 18

JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling

arXiv:2606. 19108v1 Announce Type: new Abstract: Sequence modeling has become increasingly popular in recommendation and ranking algorithms, owing to its capacity to model users' historical behaviors and infer user intentions.

By Daochen Zha, Chun How Tan, Xin Liu, Bin Xu, Han Zhao, Xiaowei Liu, Tracy Yu, Hui Gao, Huiji Gao, Liwei He, Stephanie Moyerman, Sanjeev Katariya

rag

arXiv Machine Learning

1d ago

SIFT: Search Intent-to-Filter Transformer for Multi-Task Personalized Filter Ranking at Airbnb

arXiv:2610.07810v1 Announce Type: new Abstract: Search filters help guests navigate vast catalogs in two-sided marketplaces like Airbnb, and recommending the right filters can meaningfully lift booki...

By Shashank Dabriwal, Tanya Piplani, Hao Li, Yiwei Wang, Ashish Jain, Kedar Bellare, Stephanie Moyerman

llms data-engineering

arXiv AI

Jun 8

Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations

arXiv:2606. 06779v1 Announce Type: cross Abstract: In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation.

By Nimesh Sinha, Raghav Saboo, Martin Wang, Sudeep Das

llms rag

Hugging Face Trending Papers

Jun 9

Generative Archetype-Grounded Item Representations for Sequential Recommendation

Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck.

llms rag benchmarks

arXiv Machine Learning

Jun 10

Generative Archetype-Grounded Item Representations for Sequential Recommendation

arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.

By Yifan Li, Jiahong Liu, Xinni Zhang, Hao Chen, Yankai Chen, Wenhao Yu, Jianting Chen, Irwin King

llms rag benchmarks

arXiv AI

Sep 1

SemPOI-RL: Aligning LLM Semantic Reasoning for Interpretable Out-of-Town POI Sequential Generation

arXiv:2608.30399v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong semantic reasoning and open-ended generation abilities, but aligning these abilities with structured sequ...

By Yunqi Liu, Yang Zhang, Ruixing Zhang, Liangzhe Han, Yi Qiao, Tongyu Zhu, Leilei Sun

llms reinforcement-learning fine-tuning safety

One email a morning, machine-written

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JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling

SIFT: Search Intent-to-Filter Transformer for Multi-Task Personalized Filter Ranking at Airbnb

Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations

Generative Archetype-Grounded Item Representations for Sequential Recommendation

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SemPOI-RL: Aligning LLM Semantic Reasoning for Interpretable Out-of-Town POI Sequential Generation