arXiv research

Adaptive clustering and personalization algorithms minimize regret in multi-agent stochastic linear bandits.

New method adapts to user preferences dynamically, improving recommendation models.

Model user preferences for conversational LLMs using weak rewards.

Consider an assistive system that guides visually impaired users through speech and haptic feedback to their destination. Existing robotic and ubiquitous navigation technologies (e.g., portable, ground, or wearable systems) often operate in a generic, user-agnostic manner. However, to minimize confusion and navigation …

Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired. Visual browsing systems allow e-commerce platforms to address these scenarios while offering the user an engaging shopping experience. Here we …

Real-time personalization for HAR models learns from new users without prior data.

RL platform enhances user journeys in healthcare apps.

In this paper we propose to solve an important problem in recommendation -- user cold start, based on meta leaning method. Previous meta learning approaches finetune all parameters for each new user, which is both computing and storage expensive. In contrast, we divide model parameters into fixed and adaptive parts and…

New bandit algorithms adapt to evolving user interests influenced by social circles.

Paper fine-tunes LLMs using user edits, unifying preference, supervision, and reward feedback.

We study the problem of designing AI agents that can robustly cooperate with people in human-machine partnerships. Our work is inspired by real-life scenarios in which an AI agent, e.g., a virtual assistant, has to cooperate with new users after its deployment. We model this problem via a parametric MDP framework where…

AIPS improves ranking policy evaluation by adapting to diverse user behavior.

Adaptive querying learns user psychometrics with AI personas.

Nowadays, users open multiple accounts on social media platforms and e-commerce sites, expressing their personal preferences on different domains. However, users' behaviors change across domains, depending on the content that users interact with, such as movies, music, clothing and retail products. In this paper, we pr…

Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user. We observe this ubiquitous phenomenon on both public and private datasets and address the model selection problem in pursuit of optimizi…

Personalized federated learning adapts models to each user's data.

A new deep learning framework improves HAR with user adaptation.

Recommender systems are widely used to recommend the most appealing items to users. These recommendations can be generated by applying collaborative filtering methods. The low-rank matrix completion method is the state-of-the-art collaborative filtering method. In this work, we show that the skewed distribution of rati…

We study the problem of clustering a set of items from binary user feedback. Such a problem arises in crowdsourcing platforms solving large-scale labeling tasks with minimal effort put on the users. For example, in some of the recent reCAPTCHA systems, users clicks (binary answers) can be used to efficiently label imag…

A new algorithm adapts to changing user behaviors in finance.

A new algorithm for personalized recommendations adapts to changing user interests.

Algorithm improves query recommendations with immediate user feedback.

This paper introduces Seeker, a system that allows users to interactively refine search rankings in real time, through feedback in the form of likes and dislikes. When searching online, users may not know how to accurately describe their product of choice in words. An alternative approach is to search an embedding spac…

In order to be useful, visualizations need to be interpretable. This paper uses a user-based approach to combine and assess quality measures in order to better model user preferences. Results show that cluster separability measures are outperformed by a neighborhood conservation measure, even though the former are usua…

IntelligentPooling learns personalized mHealth policies from limited data.

CoLoRA leverages task similarity to boost fine-tuning efficiency.

Paper speeds up large foundation models for time series data.

Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other parties, offering advantages in both scale and privacy. We propose a framework to augment this collaborative model-building with per-user dom…

TPG-DNN predicts user intent using GRU loss and multi-task learning.

Graph neural networks optimize radio resource management policies for wireless networks.

New algorithm balances user reward and statistical inference by mixing TS with UR based on difference size.

Semi-supervised method enhances recommendation from sparse data.

New algorithm optimizes privacy and utility in multi-task learning with skewed data.

New findings link causal models to strategic classification, improving robustness and alignment.

New algorithm optimizes beam and rate allocation in mmWave systems for multiple users.

A recommendation framework helps users choose healthcare interventions.

This work analyzes how users and services adapt to reduce risk, leading to specialization.

ADER addresses continual learning in session-based recommendation by periodically replaying exemplars with adaptive distillation.

Data science predicts user interest for midwifery content.

Personalized recommendation systems (RS) are extensively used in many services. Many of these are based on learning algorithms where the RS uses the recommendation history and the user response to learn an optimal strategy. Further, these algorithms are based on the assumption that the user interests are rigid. Specifi…

In a voice-controlled smart-home, a controller must respond not only to user's requests but also according to the interaction context. This paper describes Arcades, a system which uses deep reinforcement learning to extract context from a graphical representation of home automation system and to update continuously its…

Proposes using frequent sequences to improve sequential recommendation models.

Recommendation systems have been integrated into the majority of large online systems. They tailor those systems to individual users by filtering and ranking information according to user profiles. This adaptation process influences the way users interact with the system and, as a consequence, increases the difficulty …

In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal p…

Adversarial validation detects concept drift in user targeting systems.

This paper analyzes user-level local differential privacy in distributed systems.

Unified deep framework for personalized recommendations with uncertainty.

Social-based recommendation systems exploit the selections of friends to combat the data sparsity on user preferences, and improve the recommendation accuracy of the collaborative filtering strategy. The main challenge is to capture and weigh friends' preferences, as in practice they do necessarily match. In this paper…