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Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Learning Social Software

Publications associées (12)

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Dynamic Personalized Ranking

Jérémie Rappaz

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 ...

EPFL2022

p Recommender systems: Past, present, future

Pearl Pu Faltings

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 ...

AMER ASSOC ARTIFICIAL INTELL2021

Lifelong Machine Learning with Data Efficiency and Knowledge Retention

Fei Mi

Artificial intelligence (AI) and machine learning (ML) have become de facto tools in many real-life applications to offer a wide range of benefits for individuals and our society. A classic ML model is typically trained with a large-scale static dataset in ...

EPFL2021

Unified and Scalable Incremental Recommenders with Consumed Item Packs

Rachid Guerraoui, Rhicheek Patra

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 ...

Springer2019

Enhancing Social Media Platforms for Educational and Humanitarian Knowledge Sharing

Andrii Vozniuk

Social media (SM) platforms have demonstrated their ability to facilitate knowledge sharing on the global scale. They are increasingly often employed in educational and humanitarian domains where, despite their general benefits, they expose challenges pecu ...

EPFL2017

Collaborative Recurrent Neural Networks for Dynamic Recommender Systems

Matthias Grossglauser, Lucas Maystre, Young Jun Ko

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 ...

2016

Learning Explainable User Sentiment and Preferences for Information Filtering

Nikolaos Pappas

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 ...

EPFL2016

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

Rachid Guerraoui, Anne-Marie Kermarrec

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 ...

Assoc Computing Machinery2014

Distributed user profiling via spectral methods

Dan-Cristian Tomozei

User profiling is a useful primitive for constructing personalised services, such as content recommendation. In the present paper we investigate the feasibility of user profiling in a distributed setting, with no central authority and only local informatio ...

2014

Leveraging over prior knowledge for online learning of visual categories

Barbara Caputo, Tatiana Tommasi, Mohsen Kaboli

Open ended learning is a dynamic process based on the continuous analysis of new data, guided by past experience. On one side it is helpful to take advantage of prior knowledge when only few information on a new task is available (transfer learning). On th ...

2012