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20182024
most citedEnhancing Translation Language Models with Word Embedding for Information Retrieval

5 citations · 8 across the 3 of their papers we have counts for

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6 papers · 1 filter

cs.IR2024

Graph Reasoning for Explainable Cold Start Recommendation

Jibril Frej, Marta Knezevic, Tanja Kaser

The cold start problem, where new users or items have no interaction history, remains a critical challenge in recommender systems (RS). A common solution involves using Knowledge G…

cs.IR2024

Course Recommender Systems Need to Consider the Job Market

Jibril Frej, Anna Dai, Syrielle Montariol +2

Current course recommender systems primarily leverage learner-course interactions, course content, learner preferences, and supplementary course details like instructor, institutio…

cs.IR2023

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Jibril Frej, Neel Shah, Marta Knežević +2

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networ…

cs.IR2020

Learning Term Discrimination

Jibril Frej, Phillipe Mulhem, Didier Schwab +1

Document indexing is a key component for efficient information retrieval (IR). After preprocessing steps such as stemming and stop-word removal, document indexes usually store term…

cs.IR2019

WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset

Jibril Frej, Didier Schwab, Jean-Pierre Chevallet

Over the past years, deep learning methods allowed for new state-of-the-art results in ad-hoc information retrieval. However such methods usually require large amounts of annotated…

cs.IR20185 cited

Enhancing Translation Language Models with Word Embedding for Information Retrieval

Jibril Frej, Jean-Pierre Chevallet, Didier Schwab

In this paper, we explore the usage of Word Embedding semantic resources for Information Retrieval (IR) task. This embedding, produced by a shallow neural network, have been shown…