arXiv research

PMD measures user distances using optimal transportation, improving recommendation accuracy.

Motivated by an application of eliciting users' preferences, we investigate the problem of learning hemimetrics, i.e., pairwise distances among a set of $n$ items that satisfy triangle inequalities and non-negativity constraints. In our application, the (asymmetric) distances quantify private costs a user incurs when s…

Analysis of opinion dynamics in social networks plays an important role in today's life. For applications such as predicting users' political preference, it is particularly important to be able to analyze the dynamics of competing opinions. While observing the evolution of polar opinions of a social network's users ove…

New gait segmentation method identifies users and adversaries with high accuracy.

FISHDBC clusters arbitrary data with flexible, scalable, and hierarchical features.

This paper learns user preferences from comparisons using Mahalanobis metrics.

Model learns metrics and preferences from user comparisons.

The paper proposes a method to learn user representations invariant to social media behavior changes.

Study metric learning from limited preference comparisons, showing how low-dimensional structure can still reveal metric information.

WCF uses Wasserstein distance to recommend cold-start items based on content similarity.

In this paper, we introduce a methodology that allows to model behavioral trajectories of users in online social media. First, we illustrate how to leverage the probabilistic framework provided by Hidden Markov Models (HMMs) to represent users by embedding the temporal sequences of actions they performed online. We the…

This research converts visual information into audio for users to perceive.

We construct the Google matrices of bitcoin transactions for all year quarters during the period of January 11, 2009 till April 10, 2013. During the last quarters the network size contains about 6 million users (nodes) with about 150 million transactions. From PageRank and CheiRank probabilities, analogous to trade imp…

Growing amounts of online user data motivate the need for automated processing techniques. In case of user ratings, one interesting option is to use neural networks for learning to predict ratings given an item and a user. While training for prediction, such an approach at the same time learns to map each user to a vec…

The paper proposes a new method for product recommendation that considers revenue contributions and user similarity.

Unified framework for multi-user bandits using Laplacian kernels.

Develops a real-time exercise recommendation system using deep learning.

A new method for precise user targeting in advertising using hyperbolic manifold learning.

BLOB combines organic and bandit signals for better user interest estimation.

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

New privacy mechanism for user-level discrete distributions with reduced penalty.

Helps visually impaired users make better decisions by adjusting their observations.

Data poisoning attacks can fool neighborhood-based recommender systems.

Normalized nonnegative models assign probability distributions to users and random variables to items; see [Stark, 2015]. Rating an item is regarded as sampling the random variable assigned to the item with respect to the distribution assigned to the user who rates the item. Models of that kind are highly expressive. F…

Suppose that we wish to estimate a user's preference vector $w$ from paired comparisons of the form "does user $w$ prefer item $p$ or item $q$?," where both the user and items are embedded in a low-dimensional Euclidean space with distances that reflect user and item similarities. Such observations arise in numerous se…

Content-based news recommendation systems need to recommend news articles based on the topics and content of articles without using user specific information. Many news articles describe the occurrence of specific events and named entities including people, places or objects. In this paper, we propose a graph traversal…

This paper optimizes clustering interpretability by balancing value and user-defined features.

OTT services are replacing traditional telecom services, affecting revenue streams.

This paper introduces a new method to compare collections of distributions on manifolds and graphs.

An empirical investigation of active/continuous authentication for smartphones is presented in this paper by exploiting users' unique application usage data, i.e., distinct patterns of use, modeled by a Markovian process. Variations of Hidden Markov Models (HMMs) are evaluated for continuous user verification, and chal…

Models like support vector machines or Gaussian process regression often require positive semi-definite kernels. These kernels may be based on distance functions. While definiteness is proven for common distances and kernels, a proof for a new kernel may require too much time and effort for users who simply aim at prac…

Preference are central to decision making by both machines and humans. Representing, learning, and reasoning with preferences is an important area of study both within computer science and across the sciences. When working with preferences it is necessary to understand and compute the distance between sets of objects, …

EPIC quantifies reward differences without policy optimization.

Storytelling algorithms aim to 'connect the dots' between disparate documents by linking starting and ending documents through a series of intermediate documents. Existing storytelling algorithms are based on notions of coherence and connectivity, and thus the primary way by which users can steer the story construction…

New model accounts for continuous human trajectories in robotics.

New bounds for LDP with heterogeneous privacy levels guaranteeing high probability of accuracy.

Federated CTMC model estimates bridge deterioration hazards without sharing raw data.

New algorithm clusters data and learns kernels without relaxing constraints.

Polestar optimizes public transportation routes for efficiency and user satisfaction.

New algorithms learn stable matchings from uncertain user preferences.

LLM sandbox and persona dynamics create unethical reality gaps that shift risk to users.

A new method compares synthetic power networks to actual ones using multiscale flat norm.

Facial recognition is a key enabling component for emerging Internet of Things (IoT) services such as smart homes or responsive offices. Through the use of deep neural networks, facial recognition has achieved excellent performance. However, this is only possibly when trained with hundreds of images of each user in dif…

Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints. Instead, in many real-world applications, the constraints are derived from side information, such as users' implicit feedbacks and citations among articles. As a result, these constra…

We examined the use of modern Generative Adversarial Nets to generate novel images of oil paintings using the Painter By Numbers dataset. We implemented Spectral Normalization GAN (SN-GAN) and Spectral Normalization GAN with Gradient Penalty, and compared their outputs to a Deep Convolutional GAN. Visually, and quantit…

We consider the problem of search through comparisons, where a user is presented with two candidate objects and reveals which is closer to her intended target. We study adaptive strategies for finding the target, that require knowledge of rank relationships but not actual distances between objects. We propose a new str…

A new algorithm reduces the size of datasets for TDA.

To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information. In general, the attributes are not isolated but connected with each other, w…