Long-Tail Recommendation

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10 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

PACERec: Progressive Adaptation of LLM Item Embeddings with Collaborative Evidence for Recommendation
Nov 2, 2026Junzhao Huang, Xi Wu, Zihan Wang +9Sequential Recommendation Large Language Model-Based Recommendation
School of Computer Science and Engineering, Northeastern University, Shenyang, China · Ant Group, China
JevRec: Linguistic Encoding with Jev-like Decision Modeling for LLM-based Generative Recommendation
Oct 27, 2026Qiyong Zhong, Jiajie Su, Ming Yang +1Generative Recommendation Large Language Model-Based Recommendation
Zhejiang University, Hangzhou, China
HASTE: Hierarchical and Attribute-aware Supervision for Generative Recommendation
Oct 12, 2026Tripti Shukla, Shwetha Somasundaram, Akshat Gupta +3Long-Tail Recommendation Recommender Systems
Glance Bengaluru, India · Glance Vilnius, Lithuania · Glance London, United Kingdom
Soft Curriculum Learning for Optimizing Fresh and Generalized Recommendations
Aug 11, 2026Arnab Bhadury, Siyan Zheng, Anlan Yu +6Popularity Bias Long-Tail Recommendation
Google Vancouver, BC, Canada · Google Mountain View, CA, USA
MESH: Scaling Up Retrieval with Heterogeneous Content Unification
Jul 14, 2026Jiaxing Qu, Yilin Chen, Junpeng Hou +4Long-Tail Recommendation Recommender Systems
Pinterest Inc. San Francisco, CA, USA
Serving the Long Tail: Training-Free LLM Candidate Generation for Vacation Rental Marketplaces
Jul 10, 2026Syed Mohammed Arshad Zaidi, Eric Rincon, Shayan HassantabarSemantic Search Long-Tail Recommendation
Expedia Group Austin, Texas, USA
Distributional Approximate Nearest Neighbour Search for Uncertainty-Aware Retrieval
Jun 3, 2026Olivier JeunenLong-Tail Recommendation Recommender Systems
aampe · Antwerp, Belgium
Taming the Long Tail: Efficient Item-wise Sharpness-Aware Minimization for LLM-based Recommender Systems
Mar 13, 2026Jiaming Zhang, Yuyuan Li, Xiaohua Feng +4Long-Tail Recommendation Recommender Systems
Zhejiang University Hangzhou, Zhejiang, China · Hangzhou Dianzi University Hangzhou, Zhejiang, China · Ant Group Hangzhou, Zhejiang, China
Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation
Mar 3, 2026Chongjun Xia, Xiaoyu Shi, Hong Xie +3Long-Tail Recommendation Fairness in Recommender Systems
Chinese Academy of Sciences · University of Technology Sydney · University of Science and Technology of China
The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation
Dec 11, 2025Ziwei Liu, Yejing Wang, Wanyu Wang +6Semantic ID Recommendation Sequential Recommendation
City University of Hong Kong Hong Kong, China · Tongji University Shanghai, China · Xi’an Jiaotong University Xi’an, China +3

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PACERec: Progressive Adaptation of LLM Item Embeddings with Collaborative Evidence for Recommendation

Junzhao Huang, Xi Wu, Zihan Wang, Zijing Wang+8

School of Computer Science and Engineering, Northeastern University, Shenyang, China · Ant Group, China

Sequential Recommendation Large Language Model-Based Recommendation Long-Tail Recommendation

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Large language model (LLM) embeddings have been increasingly adopted in sequential recommendation to encode static item attributes and alleviate the limitations of item ID embeddings under sparse interactions. However, these embeddings typically capture only static content similarity while neglecting sequential transitions, and since their adaptation quality relies on sparse per-item updates or global geometric calibration, existing methods still struggle to explicitly transfer preceding interaction contexts from frequent to long-tail items under unevenly distributed behavioral evidence, limiting recommendation effectiveness across the catalog. To address this challenge, we propose PACERec, an ID-free progressive adaptation framework that incorporates preceding interaction contexts into semantic representation adaptation by learning a shared velocity field from items with sufficient interactions via frequency-weighted flow matching. In this way, PACERec progressively guides static LLM embeddings toward collaborative behavioral targets while preserving original item semantics, overcoming sparse per-item updates to effectively generalize sequential transition patterns to long-tail items for both user modeling and candidate scoring. Extensive experiments on three real-world datasets across three sequential backbones demonstrate the superiority of our method over competitive baselines in both overall and long-tail recommendation.