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KASANDR

Sumit Sidana, Charlotte Laclau, Massih R. Amini, Gilles Vandelle, André Bois-Crettez
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
month. is dataset gathers implicit feedback, in form of clicks, of users that have interacted with over 56 million o ers displayed by Kelkoo, along with a rich set of contextual features regarding both  ...  In conjunction with a detailed description of the dataset, we show the performance of six state-of-the-art recommender models and raise some questions on how to encompass the existing contextual information  ...  ACKNOWLEDGEMENT We thank FEDER for having nanced in part of the Calypso project that helped the creation of this dataset.  ... 
doi:10.1145/3077136.3080713 dblp:conf/sigir/SidanaLAVB17 fatcat:zsurgdtgjzcrlpcndpf72havgi

Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems [article]

Alexandra Burashnikova, Yury Maximov, Massih-Reza Amini
2019 arXiv   pre-print
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.  ...  Furthermore, experimental results on five large-scale collections demonstrate the efficiency of the proposed algorithm with respect to the state-of-the-art approaches, both regarding different ranking  ...  Moreover, the factor model MF which predicts clicks by matrix completion is less effective when dealing with implicit feedback than ranking based models especially on large datasets where there are fewer  ... 
arXiv:1902.08495v1 fatcat:uuaged7tdzd5dogq3a24zjehh4

Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems [article]

Sumit Sidana, Mikhail Trofimov, Oleg Horodnitskii, Charlotte Laclau, Yury Maximov, Massih-Reza Amini
2018 arXiv   pre-print
We also demonstrate that our approach is very competitive with the best state-of-the-art collaborative filtering techniques proposed for implicit feedback.  ...  The proposed model is by nature suitable for implicit feedback and involves the estimation of only very few parameters.  ...  However, in the setting of all offers, optimizing both losses simultaneously is beneficial in case of true implicit feedback datasets such as KASANDR(recall that all other datasets were synthetically made  ... 
arXiv:1705.00105v4 fatcat:vzbknaybfff73d3okj3v5zau5q

Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation

Aleksandra Burashnikova, Yury Maximov, Marianne Clausel, Charlotte Laclau, Franck Iutzeler, Massih-Reza Amini
2021 The Journal of Artificial Intelligence Research  
In this paper, we propose a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks.  ...  Furthermore, we provide a convergence analysis of both algorithms and demonstrate their practical efficiency over six large-scale collections with respect to various ranking measures and computational  ...  Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001).  ... 
doi:10.1613/jair.1.12562 fatcat:npcypmwkfrasnpfa2iw42dq2jy

Large-Scale Sequential Learning for Recommender and Engineering Systems [article]

Aleksandra Burashnikova
2022 arXiv   pre-print
For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions.  ...  To demonstrate the empirical efficiency of the proposed approaches we investigate their applications for decision making in recommender systems and energy systems domains.  ...  theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback mainly in the form of clicks.  ... 
arXiv:2205.06893v1 fatcat:rgeztuzza5dgraaxv25ngkmfta