Zobrazeno 1 - 10
of 193
pro vyhledávání: '"Nasraoui, Olfa"'
Recommendation algorithms have been pivotal in handling the overwhelming volume of online content. However, these algorithms seldom consider direct user input, resulting in superficial interaction between them. Efforts have been made to include the u
Externí odkaz:
http://arxiv.org/abs/2401.03605
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA
Bidirectional Transformer architectures are state-of-the-art sequential recommendation models that use a bi-directional representation capacity based on the Cloze task, a.k.a. Masked Language Modeling. The latter aims to predict randomly masked items
Externí odkaz:
http://arxiv.org/abs/2301.09210
Autor:
Nasraoui, Olfa
Publikováno v:
free to MU campus, to others for purchase.
Thesis (Ph. D.)--University of Missouri-Columbia, 1999.
Typescript. Vita. Includes bibliographical references (leaves 196-200). Also available on the Internet.
Typescript. Vita. Includes bibliographical references (leaves 196-200). Also available on the Internet.
Externí odkaz:
http://wwwlib.umi.com/cr/mo/fullcit?p9953886
Publikováno v:
Fifteenth ACM Conference on Recommender Systems (RecSys '21), September 27-October 1, 2021, Amsterdam, Netherlands. ACM, New York, NY, USA
Recent work in recommender systems has emphasized the importance of fairness, with a particular interest in bias and transparency, in addition to predictive accuracy. In this paper, we focus on the state of the art pairwise ranking model, Bayesian Pe
Externí odkaz:
http://arxiv.org/abs/2107.14768
The closed feedback loop in recommender systems is a common setting that can lead to different types of biases. Several studies have dealt with these biases by designing methods to mitigate their effect on the recommendations. However, most existing
Externí odkaz:
http://arxiv.org/abs/2008.13526
Autor:
Khenissi, Sami, Nasraoui, Olfa
What we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated machine learned predictions. Similarly, the predictive accuracy of learning machines heavily depends on the feedback data th
Externí odkaz:
http://arxiv.org/abs/2001.04832
Autoencoders are a common building block of Deep Learning architectures, where they are mainly used for representation learning. They have also been successfully used in Collaborative Filtering (CF) recommender systems to predict missing ratings. Unf
Externí odkaz:
http://arxiv.org/abs/2001.04344
Autor:
Damak, Khalil, Nasraoui, Olfa
State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next u
Externí odkaz:
http://arxiv.org/abs/1907.01640