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Recommanding sequences in a multidimensional space

Pierre-Edouard Osche 1 
1 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : Recommender systems represent a fundamental research field situated at the intersection of several major disciplines such as: machine learning, human-computer interaction and cognitive sciences. The objective of these systems is to improve interactions between the user and information access or retrieval systems. Facing heterogeneous and ever-increasing data, indeed it has become difficult for a user to access relevant information which would be satisfying his requests. Current systems have proven their added value, and they rely on various learning techniques. Nevertheless, despite temporal and spatial modeling has been made possible, state of the art models which are dealing with the order of recommendations or with the quality of a recommendation sequence are still too rare. In this thesis, we will focus on defining a new formalism and a methodological framework allowing: (1) the definition of human factors leading to decision making and user satisfaction; (2) the construction of a generic and multi-criteria model integrating these human factors with the aim of recommending relevant resources in a coherent sequence; (3) a holistic evaluation of user satisfaction with their recommendation path. The evaluation of recommendations, all domains combined, is currently done on a recommendation-by-recommendation basis, with each evaluation metric taken independently. The aim is to propose a more complete framework measuring the evolutivity and comprehensiveness of the path. Such a multi-criteria recommendation model has many application areas. For example, it can be used in the context of online music listening with the recommendation of intelligent and adaptive playlists. It can also be useful to adapt the recommendation path to the learner's progress and to the teacher's pedagogical scenario in an e-learning context.
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Submitted on : Thursday, June 3, 2021 - 12:50:43 PM
Last modification on : Saturday, October 16, 2021 - 11:26:08 AM
Long-term archiving on: : Saturday, September 4, 2021 - 6:37:09 PM


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  • HAL Id : tel-03248061, version 1


Pierre-Edouard Osche. Recommanding sequences in a multidimensional space. Computer Science [cs]. Université de Lorraine, 2021. English. ⟨NNT : 2021LORR0070⟩. ⟨tel-03248061⟩



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