Combining Content with User Preferences for Non-Fiction Multimedia Recommendation: A Study on TED Lectures

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This paper examines how the European press dealt with the no-vax reactions against the Covid-19 vaccine and the dis- and misinformation associated with this movement. Using a curated dataset of 1786 articles from 19 European newspapers on the anti-vaccine ...

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 ...

Background. In medicine and other applications, the copying and sharing of data is impractical for a range of well-considered reasons. With federated learning (FL) techniques, machine learning models can be trained on data spread across several locations w ...

Making decisions is part and parcel of being human. Among a set of actions, we want to choose the one that has the highest reward. But the uncertainty of the outcome prevents us from always making the right decision. Making decisions under uncertainty can ...

Recommenders personalize the web content using collaborative filtering to relate users (or items). This work proposes to unify user-based, item-based and neural word embeddings types of recommenders under a single abstraction for their input, we name Consu ...

The promise of digitization of historical archives lies in their indexation at the level of contents. Unfortunately, this kind of indexation does not scale, if done manually. In this article we present a method to bootstrap the deployment of a content-base ...

In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on th ...

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 ...

Communication technology has advanced up to a point where children are getting unfamiliar with themost iconic symbol in IT: the loading icon. We no longer wait for something to come on TV, nor for a download to complete. All the content we desire is availa ...

Finding relevant content is one of the core activities of users interacting with a content repository, be it knowledge workers using an organizational knowledge management system at a workplace or self-regulated learners collaborating in a learning environ ...

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 ...

Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...

In recent years, the use of 3D digital content becomes widespread in various industrial and scientific domains. However, content creation still remains a costly task as extensive manual work is often required. As such, one of the core research topics in co ...

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 ...

By leveraging crowdsourcing, Web credibility evaluation systems (WCESs) have become a promising tool to assess the credibility of Web content, e.g., Web pages. However, existing systems adopt a passive Way to collect users' credibility ratings, which incur ...

Recent works on word representations mostly rely on predictive models. Distributed word representations (aka word embeddings) are trained to optimally predict the contexts in which the corresponding words tend to appear. Such models have succeeded in captu ...

Blogs are one of the most prominent means of communication on the web. Their content, interconnections and influence constitute a unique socio-technical artefact of our times which needs to be preserved. The BlogForever project has established best practic ...

The rapidly increasing amount of user-generated content in social tagging systems provides a huge source of information. Yet, performing effective search in these systems is very challenging, especially when we seek the most appropriate items that match a ...

The Internet has become an important source of information that significantly affects social, economical and political life. The content available in the Web is the basis for the operation of the digital economy. Moreover, Web content has become essential ...

Recommender systems have emerged, as an intelligent information filtering tool, to help users effectively identify information items of interest from a set of overwhelming choices and provide personalized services. Most recommendation technologies typicall ...

Combining Content with User Preferences for Non-Fiction Multimedia Recommendation: A Study on TED Lectures

Chattez avec Graph Search

Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework

Dynamic Personalized Ranking

Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning

Multi-armed Bandits in Action

Unified and Scalable Incremental Recommenders with Consumed Item Packs

Index-Driven Digitization and Indexation of Historical Archives

Spherical Clustering of Users Navigating 360° Content

Towards Scalable Personalization

A Gaussian Source Coding Perspective on Caching and Total Correlation

Combining Content Analytics and Activity Tracking to Identify User Interests and Enable Knowledge Discovery

Learning Explainable User Sentiment and Preferences for Information Filtering

Word Embeddings for Natural Language Processing

Analysis and Synthesis of Structured Variations in 3D Geometries

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

Towards a highly effective and robust Web credibility evaluation system

Rehabilitation of Count-based Models for Word Vector Representations

A Scalable Approach to Harvest Modern Weblogs

Personalizing Top-k Processing Online in a Peer-to-Peer Social Tagging Network

Studying Web Content Credibility by Social Simulation

Design and User Perception Issues for Personality-Engaged Recommender Systems