136 citations · 448 across the 41 of their papers we have counts for
6 papers · 1 filter
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)
Aleksandra Burashnikova, Yury Maximov, Marianne Clausel +3
This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012.06910], where we proposed a theoretically supported sequential strategy for training a large-scale Reco…
Recommender systems: when memory matters
Aleksandra Burashnikova, Marianne Clausel, Massih-Reza Amini +2
In this paper, we study the effect of long memory in the learnability of a sequential recommender system including users' implicit feedback. We propose an online algorithm, where m…
Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation
Aleksandra Burashnikova, Marianne Clausel, Charlotte Laclau +3
In this paper, we propose a theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback, mainly in the form of clicks. The pr…
Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems
Alexandra Burashnikova, Yury Maximov, Massih-Reza Amini
In this paper, we propose a robust sequential learning strategy for training large-scale Recommender Systems (RS) over implicit feedback mainly in the form of clicks. Our approach…
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Georgios Balikas, Simon Moura, Massih-Reza Amini
Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately. We argue that suc…
LSHTC: A Benchmark for Large-Scale Text Classification
Ioannis Partalas, Aris Kosmopoulos, Nicolas Baskiotis +6
LSHTC is a series of challenges which aims to assess the performance of classification systems in large-scale classification in a a large number of classes (up to hundreds of thous…