arXiv Machine Learning By Zhongkai Shangguan, Yanbin Kang, Yihan Cao, Ruijie Jiang, Qiannan Yin, Chang Yue, Zijian Zhou, Girish Kathalagiri, Changshuai Wei, Zhengming Xing

PAIR: Preference-Aligned Intent Retrieval for Premium People Recommendation at LinkedIn

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arXiv Machine Learning
Jul 31

OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval

arXiv:2607. 27475v1 Announce Type: cross Abstract: In modern recommendation systems, retrieval serves as a primary stage responsible for filtering billions of candidate items down to thousands prior to refined ranking.

By Ziwei Li, Shuyao Li, Xufeng Cai, Xue Zou, Yiming Ma, Huiting Lu, Wujie Yan, Zhichen Zhao, Yang Lu, Zhe Wang, Rui Luo, Zhengyu Su, Dan Zhang, Ji Liu
arXiv AI
Sep 2

VIBE-Bench: Evaluating Personalized Large Language Models When Profiles Don't Mean Preferences

The paper introduces VIBE‑Bench, a new benchmark designed to test personalized large language models (PLLMs) in a regime where user profile cues and query‑specific preferences do not share the same conceptual space, a situation termed profile‑preference conceptual misalignment (PRCM). VIBE‑Bench contains 3,504 personas, 12,239 dialogues, and a manually verified gold test set, and includes two psychology‑grounded tasks that require cross‑concept preference reasoning beyond surface semantic overlap. Experiments show that existing PLLMs largely depend on shallow semantic correlations and struggle to learn robust cross‑concept mappings, highlighting PRCM as a distinct failure mode for personalization models.

By Yiwen Jiang, Yang Deng, Stephanie Fong, Zimu Wang, Yaling Shen, Wei Feng, Hongxi Yang, Xiangyu Zhao, Zhongxing Xu, Deval Mehta, Xuelian Cheng, Zongyuan Ge
arXiv AI
Sep 10

Less Is Personal: Learning Minimal Sufficient User Profiles for Personalized Language Models

The paper introduces ENOUGH, a method for creating minimal sufficient user profiles for personalized language models. ENOUGH iteratively adds behavioral records or stops, evaluating profile prefixes with a counterfactual search that balances downstream gains, user specificity, and token costs. The resulting profiles are distilled into a lightweight controller that orders records and triggers the generator only once, achieving better effectiveness and efficiency than existing baselines across six tasks.

By Minghang Liu, Qiang Qiu, Yuanzhuo Wang, Huawei Shen, Xueqi Cheng
arXiv Machine Learning
Oct 9

LIME: Link-based User-item Interaction Modeling with Decoupled XOR Attention for Efficient Test Time Scaling

LIME is a new recommendation system architecture that tackles the high computational cost of transformers by introducing low‑rank link embeddings and a linear attention mechanism called LIME‑XOR. These innovations decouple user and candidate interactions, allowing pre‑computation of attention weights and reducing inference complexity to be nearly independent of candidate set size and linear in user sequence length. Experiments on public and industrial datasets demonstrate that LIME matches transformer performance while achieving a tenfold speedup on large candidate sets or long sequences, and it has been shown to improve user engagement on a major recommendation platform.

By Yang Liu, Yunjiang Jiang, Ayush Agarwal, Yihan Wu, Haoran Liu, Bi Xue