#personalization

16 curated events

papersTODAY 04:00 UTC

arXiv Paper Proposes Efficient Personalization for Generative User Interfaces

A new arXiv paper examines how generative user interfaces (GenUIs), which build interface layouts on demand, can be tailored to individual users. The authors note that conventional personalization through predefined settings is impractical because screens are created dynamically rather than designed in advance. Their work proposes an efficient approach to personalizing these generated interfaces, and the preprint is cross-listed under cs.AI and cs.LG.

research/personalization/user-interfaces/arxiv

papersTODAY 04:00 UTC

BEACON: Behavior and Appearance Control for Subject-Specific Video Generation

A new arXiv paper introduces BEACON, a method for generating videos of a specific person that retains both their visual identity and their individual expressive behavior. The authors argue that beyond matching appearance, such models must also capture the facial mannerisms that distinguish how a given subject acts on camera. The work targets human-centric video synthesis where subject-specific fidelity is the main challenge.

video-generation/research/face/personalization

papersTODAY 04:00 UTC

Paper Proposes Self-Evolving Memory for Generative Recommendation

A new arXiv paper addresses how generative recommender systems can keep up with user preferences that shift over time. The authors propose a self-evolving memory mechanism intended to let these models update their behavior as tastes change. The work is categorized as a cross-list submission under arXiv cs.AI.

recommender-systems/memory/research/personalization

papersTODAY 04:00 UTC

Personalized Balance Evaluation Method Proposed for Hip Exoskeleton Walking

Researchers present a participant-specific framework for assessing dynamic balance in people walking with hip exoskeleton assistance, including trials that involve unexpected ground perturbations. The approach aims to handle the challenges of small, noisy human-in-the-loop datasets, since balance is a multidimensional quantity that is hard to personalize with conventional methods. The work appears as an arXiv preprint in machine learning.

exoskeleton/balance/personalization/arxiv

papersTODAY 04:00 UTC

CRAMER: Request-Aware Masking Method for Editing Sequential Recommenders

A revised arXiv paper introduces CRAMER, a technique for controlling sequential recommendation models through request-aware masking. The approach aims to let such models respond more flexibly to users' immediate, time-sensitive requests rather than only their long-term behavior patterns. The paper appears on arXiv's cs.AI and cs.LG listings as a cross-list replacement.

recommender-systems/arxiv/masking/personalization

papersTODAY 04:00 UTC

Compact Adaptation Method Aligns Personalized Federated Learning with Record-Level Privacy

A new arXiv paper examines how record-level differential privacy clashes with personalized federated learning, where each client's individual variation is low-dimensional but repeated training rounds broadcast high-dimensional model updates. The authors propose a compact adaptation approach paired with variable-length Gaussian communication to reduce that mismatch. The work targets more efficient and privacy-preserving personalization across federated clients.

federated-learning/differential-privacy/research/personalization

papersTODAY 04:00 UTC

Paper Proposes Generate-Then-Select Method for Personalized Headlines in Recommendation Feeds

A new arXiv paper addresses the problem that a single static headline for an item in industrial recommendation feeds fails to serve users with varied or niche interests. The authors propose a generate-to-explore, select-to-exploit approach that uses large language models to produce candidate headlines and then picks the one that best matches an individual user's preferences. The work targets long-tail audiences that are typically underserved by one-size-fits-all headlines.

llm/recommendation/headline-generation/personalization

papersTODAY 04:00 UTC

arXiv paper presents pipeline for generating personalized educational videos from textbooks

A new arXiv preprint describes an automated system that turns textbook PDFs into interactive video explanations tailored to individual questions. After a user uploads a PDF and submits a query, the pipeline produces a video answer, with the reported focus on NCERT educational materials. The work targets personalized, question-driven learning content.

education/video-generation/personalization/arxiv

papersSEP 10 04:00 UTC

Kernel-Managed Shared Memory Proposed for System-Wide AI Personalization

Researchers introduce kernel-managed shared memory, a system-level abstraction that lets an operating system kernel store and share personalized context across AI agents. The approach aims to solve the problem of useful user-specific knowledge remaining locked within a single agent in multi-agent systems. The paper was posted to arXiv's cs.AI category and cross-listed in cs.LG.

agents/personalization/operating-systems/research

papersSEP 10 04:00 UTC

NEXUS-MI: federated personalization framework for EEG motor-imagery brain-computer interfaces

Researchers present NEXUS-MI, an approach that personalizes EEG-based motor-imagery brain-computer interfaces through federated learning, letting user models improve without centralizing sensitive neural recordings. A gateway coordinates training in a communication-aware way, addressing variability across subjects and sessions. The goal is reliable adaptation when each person has only a small amount of calibration data.

federated-learning/bci/personalization/privacy

papersSEP 10 04:00 UTC

Researchers Propose Influence-Based Weighting for Personalized Federated Learning

A revised arXiv preprint introduces a personalized federated learning method that weights each client's parameter updates according to its influence, rather than relying on fixed aggregation weights. The approach is designed to let devices with different data distributions and preferences train collaboratively while keeping their data private. The updated version appears across arXiv's cs.AI and cs.LG listings.

federated-learning/privacy/personalization/machine-learning

papersSEP 10 04:00 UTC

Researchers propose minimal sufficient user profiles for personalized language models

A new arXiv paper examines how large language models personalize their outputs using records retrieved from user histories. Rather than prepending a fixed number of retrieved items, the authors learn minimal sufficient user profiles that retain only the information needed to produce accurate, preference-aligned responses.

personalization/llm/retrieval/research

papersSEP 10 04:00 UTC

Dear Algo paper describes agentic intent layer for unified search and recommendation on Threads

A new arXiv paper introduces Dear Algo, a system deployed on Threads that sits between open-ended user requests and the platform's ranking engines. It lets people steer discovery with free-form instructions such as 'more NBA news' or 'less politics', translating them into precise signals that shape both search results and recommendations. The authors outline how the intent layer is designed to handle this shared discovery objective across the two systems.

agents/search/recommendation/personalization

papersSEP 10 04:00 UTC

Paper Personalizes Small Language Models with Individual Text Corpora via RAG and DoRA Fine-Tuning

A new arXiv paper takes a cognitive-simulation approach to episodic and semantic memory by feeding text from a web-crawled individual text corpus into small language models. The authors compare retrieval-augmented generation against DoRA fine-tuning for encoding personal knowledge, evaluating performance on multiple-choice question answering.

fine-tuning/personalization/rag/small-models

papersSEP 10 04:00 UTC

PRAGMA Benchmark Evaluates Personalized Guidance with Memory Alignment in Lifelong Conversations

A new arXiv paper introduces PRAGMA, a benchmark for measuring how well large language models align stored user memory with the advice they deliver in long-running conversations. The work targets a key weakness of personalized assistants: as dialogue histories grow, working from complete logs becomes inefficient and error-prone. PRAGMA offers a standardized way to evaluate memory use in lifelong conversational systems.

benchmark/llm/memory/personalization

papersSEP 10 04:00 UTC

HyperTrace: Hypothesis-Based Preference Tracing for Online LLM Personalization

A new arXiv paper introduces HyperTrace, a training-free method for adapting large language model responses to individual users. The approach targets preferences that are latent and only revealed gradually through interaction, an area where existing techniques based on stored histories or retrieved memories reportedly fall short. The work frames personalization as a preference-tracing problem driven by hypotheses formed during ongoing conversations.

llm/personalization/training-free/research