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Collaborative Filtering Under a Sybil Attack: Similarity Metrics do Matter!

Related publications (27)

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

Smaller, Faster & Lighter KNN Graph Constructions

Rachid Guerraoui, Anne-Marie Kermarrec, Olivier Ruas

We propose GoldFinger, a new compact and fast-to-compute binary representation of datasets to approximate Jaccard’s index. We illustrate the effectiveness of GoldFinger on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and s ...

2020

On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation

Robert West, Maxime Jean Julien Peyrard, Yang Gao, Wei Zhao

Evaluation of cross-lingual encoders is usually performed either via zero-shot cross-lingual transfer in supervised downstream tasks or via unsupervised cross-lingual textual similarity. In this paper, we concern ourselves with reference-free machine trans ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2020

Towards a Point Cloud Structural Similarity Metric

Touradj Ebrahimi, Evangelos Alexiou

Point cloud is a 3D image representation that has recently emerged as a viable approach for advanced content modality in modern communication systems. In view of its wide adoption, quality evaluation metrics are essential. In this paper, we propose and ass ...

IEEE2020

Context-based Quality of Experience in Immersive Multimedia

Anne-Flore Nicole Marie Perrin

Since 1895, when the Lumière brothers came out with the projected cinematograph, motion pictures and more generally multimedia contents have considerably improved and are still experiencing a very dynamic development. Advances involved improving various mo ...

EPFL2019

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

Towards Scalable Personalization

Rhicheek Patra

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 ...

EPFL2018

Text Similarity in Vector Space Models: A Comparative Study

Kenneth Younge, Omid Shahmirzadi, Adam Lugowski

Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to ...

2018

A novel methodology for quality assessment of voxelized point clouds

Touradj Ebrahimi, Evangelos Alexiou

Recent trends in multimedia technologies indicate a significant growth of interest for new imaging modalities that aim to provide immersive experiences by increasing the engagement of the user with the content. Among other solutions, point clouds denote an ...

SPIE-INT SOC OPTICAL ENGINEERING2018

Online Efficient Bio-Medical Video Transcoding on MPSoCs Through Content-Aware Workload Allocation

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Ali Pahlevan

Bio-medical image processing in the field of telemedicine, and in particular the definition of systems that allow medical diagnostics in a collaborative and distributed way is experiencing an undeniable growth. Due to the high quality of bio-medical videos ...

IEEE and ACM Press2018