arXiv research

Deep learning optimizes user association in Massive MIMO networks.

A new method uses machine learning to optimize user pairing and association in multicell NOMA networks.

Federated learning optimizes task and resource allocation in balloon networks.

OMBA learns product and user representations for better online market basket analysis.

Paper models MOOC users behavior using MDP for easier classification.

Popular online enrichment analysis tools from the field of molecular systems biology provide users with the ability to submit their experimental results as gene sets for individual analysis. Such queries are kept private, and have never before been considered as a resource for integrative analysis. By harnessing gene s…

Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that ha…

Paper proposes using pairwise feature comparisons to infer modification costs for user recourse.

We consider the task of collaborative preference completion: given a pool of items, a pool of users and a partially observed item-user rating matrix, the goal is to recover the \emph{personalized ranking} of each user over all of the items. Our approach is nonparametric: we assume that each item $i$ and each user $u$ h…

EgalMAB solves fair resource allocation in stochastic bandits.

Study improves scalability of cell-free massive MIMO networks by optimizing UE-AP association.

MACRo-mIcro VAE learns disentangled user behavior representations.

Classifies privacy policy segments for better user understanding.

Smart app tracks relapse history and predicts relapse based on spatial-temporal factors.

Bayesian optimization agent learns user preferences from pairwise comparisons.

Deep learning identifies smartphone users from motion sensor data.

Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly over the last decade. Users' decision processes are complex and highly influenced by contextual settings, but data supporting the development…

Algorithm maximizes user rewards under per-item budget constraints.

The paper analyzes user activities in OSNs using a vector space model.

Optimal algorithm for latent bandits with cluster structure reduces regret to nearly optimal.

This paper surveys and classifies attribute-aware CF models.

Integrates regression trees to explain latent factor models in recommendation systems.

Model predicts next destination for users based on past trips and features.

Recommending items to users is a challenging task due to the large amount of missing information. In many cases, the data solely consist of ratings or tags voluntarily contributed by each user on a very limited subset of the available items, so that most of the data of potential interest is actually missing. Current ap…

RIVCoin stabilizes cryptocurrency portfolios through a DAO and redistributes income.

A deep RL framework optimizes resource allocation in wireless networks.

FedConPE improves conversational recommender systems efficiency and privacy.

PABBO optimizes user utility learning from preferential feedback, significantly faster than traditional methods.

Collaborative filtering (CF) allows the preferences of multiple users to be pooled to make recommendations regarding unseen products. We consider in this paper the problem of online and interactive CF: given the current ratings associated with a user, what queries (new ratings) would most improve the quality of the rec…

Method optimizes diffusion model generation to meet user preferences.

A new algorithm improves top-$k$ recommendation accuracy by considering item payoffs uncertainty.

Federated approach protects privacy while learning user patterns.

Recommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnerable to poisoning attacks, in which an attacker injects fake data to a given system such that the system makes recommendations as the attacke…

An interactive image retrieval system learns which images in the database belong to a user's query concept, by analyzing the example images and feedback provided by the user. The challenge is to retrieve the relevant images with minimal user interaction. In this work, we propose to solve this problem by posing it as a …

UVeQFed tackles FL model compression over limited channels.

A new method selects important variables for clustering from dependency networks.

Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-…

Proposes element-level differential privacy for better privacy and utility in statistical learning.

Bayesian Neural Networks help quantify uncertainty in deep learning predictions.

A new Input-Output model, called the Multi-Entity Input-Output (MEIO) model, is introduced to estimate the responsibility of entities of an ecosystem on the footprint of each other. It assumed that the ecosystem is comprised of end users, service providers, and utilities. The proposed MEIO modeling approach can be seen…

We tackle robust influence maximization in social networks with hyperparametric edge probabilities.

New estimator GMIPS reduces variance in ranking policy evaluation.

Study optimal bidding strategies for digital ads targeting purchases and health campaigns.

Method discovers user habits from mobile data.

A new method for federated learning with only positive labels.

Deep learning model for AI co-creativity boosts design creativity.

We address the cold start problem in recommendation systems assuming no contextual information is available neither about users, nor items. We consider the case in which we only have access to a set of ratings of items by users. Most of the existing works consider a batch setting, and use cross-validation to tune param…

The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters. Selecting an appropriate learning algorithm and setting its hyperparameters for a given data set can be a challenging task, especially for users who are not experts in …