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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.
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.
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.
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).
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...