The paper investigates privacy risks of language embeddings used in recommender systems. It demonstrates that a simple cosine‑similarity attack can recover item identities with high accuracy when an adversary has auxiliary text, even when the descriptions are independently sourced. The study also shows that such leakage can lead to user re‑identification from sparse ratings and evaluates obfuscated descriptions as a mitigation, revealing a trade‑off between privacy and utility.
By Linda Bekov\'a, Rodrigo Alves
The study reproduces a prior work on recommender systems that use generated natural‑language user profiles to enhance transparency and user control. It confirms that the User Profile Recommendation (UPR) model performs competitively and that altering these profiles uniformly shifts predicted ratings without changing ranking order. Additional experiments include context ablation, multi‑seed stability, and mechanistic interpretability analysis with the nnsight framework.
arXiv:2607. 01276v1 Announce Type: cross Abstract: Embedding models are essential components of modern Information Retrieval (IR) systems, yet they are typically hidden behind APIs.
By Cedric Fitiavana Raelijohn, S\'ebastien Gambs, Jean-Francois Rajotte
This reproducibility study confirms that incorporating generated natural‑language user profiles into recommender systems enhances transparency and allows users to directly intervene by correcting preferences or addressing cold‑start issues. The authors replicated the original findings and extended the evaluation with context ablation, multi‑seed stability tests, and mechanistic interpretability analysis using nnsight. Their results show that while perturbing profiles shifts predicted ratings uniformly across genres, the overall rankings remain unchanged, attributing this to the rating‑regression objective rather than the profile interface.
By Noah Mami\'e, Laurin van den Bergh
arXiv:2606. 07688v1 Announce Type: cross Abstract: Generative recommendation formulates next-item prediction as autoregressive generation over semantic ID (SID) sequences derived from users' historical interactions, making modern recommender systems structurally similar to large language models (LLMs).
By Ziheng Chen, Jiali Cheng, Zezhong Fan, Hadi Amiri, Diyuan Wu, Gabriele Tolomei, Yang Zhang
arXiv:2610.14487v1 Announce Type: new
Abstract: Generative recommendation models based on autoregressive decoding have been adopted as alternatives to classical ranking-based systems. While their mod...
By Zhuoxuan Zhang, Jinhao Duan, Xinyu Zhao, Jinduo Guo, Shuai Shao, Rui Li, Luke Simon, Zhun Deng, Tianlong Chen, Bo Sun
With the widespread applications of large language models (LLMs), privacy-preserving inference has become increasingly essential for sensitive queries. To balance privacy and utility, a series of ligh...
The paper introduces a lightweight privacy-preserving technique for Retrieval-Augmented Generation (RAG) systems that uses random projection to transform both datastore and query embeddings. This approach maintains semantic similarity for effective retrieval while significantly reducing the risk of data extraction attacks. Experiments across various RAG architectures and datasets demonstrate that the method outperforms prior differential privacy-based techniques in both retrieval and generation performance while offering comparable empirical privacy.
By Dixi Yao, Tahseen Rabbani, Tian Li
arXiv:2310. 16152v5 Announce Type: replace-cross Abstract: Federated learning (FL) has become a key component in various language modeling applications such as machine translation, next-word prediction, and medical record analysis.
By Md Rafi Ur Rashid, Vishnu Asutosh Dasu, Kang Gu, Najrin Sultana, Shagufta Mehnaz
arXiv:2403. 00802v2 Announce Type: replace-cross Abstract: Production-grade recommender systems rely heavily on a large-scale corpus used by online media services, including Netflix, Pinterest, and Amazon.
By Amit Kumar Jaiswal
arXiv:2606. 29064v1 Announce Type: cross Abstract: The unfairness of recommender systems has become a topic of concern due to its significant social and ethical implications.
By Yanan Wang, Yong Ge
arXiv:2509. 20324v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with external document retrieval to produce more accurate and grounded responses.
By Atousa Arzanipour, Rouzbeh Behnia, Reza Ebrahimi, Kaushik Dutta