personalization

This paper proposes Spire, a framework that formulates slide personalization as an inverse planning problem, using structural denoising and reinforcement learning to infer latent design intents without relying on explicit templates or verbose instructions.

This paper introduces Personalized Active Preference Alignment (PAPA), a method for fine-tuning diffusion models using real-time user feedback without a parameterized reward model, enhancing efficiency in personalized tasks like recommendations and image generation.

A guide to seven lesser-known Google account settings that help users manage privacy, security, and personalization across Google's apps like Gmail, Maps, and YouTube.

Built an AI agent that researches prospects and generates personalized email and LinkedIn outreach drafts in under 60 seconds, seeking feedback from SDRs and founders.

This paper presents an integrated framework for personalizing free-value thresholds in a two-sided job marketplace, addressing competing objectives and constrained experiments. The deployed system shows significant lift in target metrics while respecting engagement guardrails.

This paper presents a study on sequential LoRA personalization of Small Language Models, using checkpoint-level evaluation to monitor task performance and forgetting, and shows that lightweight reference set diagnostics can reveal instability patterns.

The article describes a novel approach to attachment extraction in AI tools where the tool builds a cognitive map of the user's thinking patterns and past interactions to automatically extract relevant information, overriding default generic extraction when explicit instructions are given.

Introduces SocialPersona, a benchmark for evaluating multimodal large language models on their ability to recover revealed preferences from longitudinal social-media timelines and use them in personalized dialogue.

Artificial intelligence is reshaping retail by embedding intelligence into decision-making processes such as search, inventory management, and customer engagement. Macy's adopts an 'AI-first' approach, integrating AI into systems to personalize experiences and improve operational efficiency.

This paper introduces a taxonomy of conversational memory types and a user-centric evaluation framework to study how different memory roles affect response quality in RAG-based conversational agents.

This paper introduces a retrieval-augmented personalization method for wearable stress detection using frozen foundation models, achieving near-supervised fine-tuning performance without requiring labeled user data.

This article proposes a feedback memory layer for AI agents that learns from every human approval or rejection, enabling continuous improvement from user interactions.

dair_ai announces a new /learn agent skill that creates a personalized learning plan and a learning hub that adapts to the learner's needs and progress.

A detailed breakdown of Netflix's hybrid weighted recommendation system design, covering scale estimation, cold start strategies for new users, behavioral signal capture, and the balance between recall and precision.

MoEngage acquires Aampe to bring AI agents that personalize marketing for each customer, moving beyond traditional segment-based campaigns. The deal aims to help MoEngage win enterprise customers from rivals like Salesforce and Adobe.

AOHP is an Android-based open-source OS framework that treats AI agents as first-class entities, improving task completion by 21.12% and reducing token costs by 51.55% through agent-oriented mechanisms like personalized service composition and secure information flow.

Proposes User as Engram, a method to store per-user memory as sparse local parametric edits in a hash-keyed memory table, inspired by hippocampal engrams, achieving better reasoning accuracy and memory efficiency compared to per-user LoRA.

The author critiques the idea of agents remembering everything and introduces TrueMemory, a system that converts memories into trait claims with confidence and evidence to better calibrate agent behavior.

This paper introduces MemSlides, a hierarchical memory framework for personalized slide generation that separates long-term user profiles, working memory for session constraints, and tool memory for localized edits, enabling multi-turn revision without full regeneration.

ChatPlanner is a novel framework that uses fine-tuned LLMs with Retrieval-Augmented Generation (RAG) to interpret user preferences from natural language queries and integrate them into public transit routing algorithms, outperforming existing route planners.