Interplay between upsampling and regularization for provider fairness in recommender systems

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Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between a discrete and a generic (possibly non-discrete) probability measure, are believed to be computationally hard. Even though such problems are ubiquitous in statistics, ...

Personalized ranking methods are at the core of many systems that learn to produce recommendations from user feedbacks. Their primary objective is to identify relevant items from very large vocabularies and to assist users in discovering new content. These ...

Diversity-aware platform design is a paradigm that responds to the ethical challenges of existing social media platforms. Available platforms have been criticized for minimizing users' autonomy, marginalizing minorities, and exploiting users' data for prof ...

We characterize the solution of a broad class of convex optimization problems that address the reconstruction of a function from a finite number of linear measurements. The underlying hypothesis is that the solution is decomposable as a finite sum of compo ...

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly ...

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenes are characterized by a prevalence of human made structures, which in most of the cases, are either ...

We study 1D continuous-domain inverse problems for multicomponent signals. The prior assumption on these signals is that each component is sparse in a different dictionary specified by a regularization operators. We introduce a hybrid regularization functi ...

This paper studies the performance of three wellknown regularization techniques to solve the inverse problem encountered in differential microwave imaging for brain stroke applications. Reconstructions from a multi-illumination configuration based on measu ...

The ever-growing amount of online information calls for Personalization. Among the various personalization systems, recommenders have become increasingly popular in recent years. Recommenders typically use collaborative filtering to suggest the most releva ...

Recommender-systems has been a significant research direction in both literature and practice. The core of recommender systems are the recommendation mechanisms, which suggest to a user a selected set of items supposed to match user true intent, based on e ...

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

Sedentary lifestyles and bad eating habits influence the onset of many serious health problems. Healthy behavior change is an arduous task, and requires a careful planning. In this thesis, we propose that behavior recommenders can help their users achieve ...

Modern technologies enable us to record sequences of online user activity at an unprecedented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating-prediction paradigm, ignoring temporal ...

Third party applications work on top of existing platforms that host users’ data. Although these apps access this data to provide users with specific services, they can also use it for monetization or profiling purposes. In practice, there is a significant ...

Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely, predictable and cost-effective analytical processing of such large data sets in order to ext ...

In the last decade, online social networks have enabled people to interact in many ways with each other and with content. The digital traces of such actions reveal people's preferences towards online content such as news or products. These traces often res ...

We consider the high energy physics unfolding problem where the goal is to estimate the spectrum of elementary particles given observations distorted by the limited resolution of a particle detector. This important statistical inverse problem arising in da ...

Product review sites such as TripAdvisor, Yelp or Amazon provide a single, non personalized ranking of products. The sparse review data makes personalizing recommendations difficult. Topic Profile Collaborative Filtering exploits review texts to identify u ...

Group recommender systems suggest items of interest to a group of people. Traditionally, group recommenders provide recommendations by aggregation the group membersâ preferences. Nowadays, there is a trend of decentralized group recommendation process th ...

The advent of online social networks created new prediction opportunities for recommender systems: instead of relying on past rating history through the use of collaborative filtering (CF), they can leverage the social relations among users as a predictor ...

Interplay between upsampling and regularization for provider fairness in recommender systems

Chattez avec Graph Search

Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution

Dynamic Personalized Ranking

The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations

Convex optimization in sums of Banach spaces

p Recommender systems: Past, present, future

Joint Graph-based Depth Refinement and Normal Estimation

Sparse Dictionaries for Continuous-Domain Inverse Problems

Comparison of Regularization Techniques for Microwave Imaging of Brain Stroke

Towards Scalable Personalization

Diversifying Group Recommendation

Applications of Approximate Learning and Inference for Probabilistic Models

Recommender Systems for Healthy Behavior Change

Collaborative Recurrent Neural Networks for Dynamic Recommender Systems

Data-Driven Privacy Indicators

Toward timely, predictable and cost-effective data analytics

Learning Explainable User Sentiment and Preferences for Information Filtering

Statistical Unfolding Of Elementary Particle Spectra: Empirical Bayes Estimation And Bias-Corrected Uncertainty Quantification

Personalizing Product Rankings Using Collaborative Filtering on Opinion-Derived Topic Profiles

Social Interface and Interaction Design for Group Recommender Systems

Comparing the Predictive Capability of Social and Interest Affinity for Recommendations