Apprentissage de préférences en espace combinatoire et application à la recommandation en configuration interactive

Abstract : The analysis and the exploitation of preferences occur in multiple domains, such as economics, humanities and psychology. E-commerce got interested in the subject a few years ago with the surge of product personalisation. Our study deals with the representation and the learning of preferences on objects described by a set of attributes. These combinatorial spaces are huge, which makes the representation of an ordering in extenso intractable. That's why preference representation languages have been built: they can represent preferences compactly on these huge spaces. In this dissertation, we study preference representation languages and preference learning.Our work focuses on two approaches. Our first approach led us to propose the DRC algorithm for inference in Bayesian networks. While other inference algorithms use the sole Bayesian network as a source of information, DRC makes use of the fact that Bayesian networks are often learnt from a set of examples either chosen or observed. Such examples are a valuable source of information that can be used during the inference. Based on this observation, DRC uses not only the Bayesian network structure that captures the conditional independences between attributes, but also the set of examples, by estimating the probabilities directly from it. DRC is particularly adapted to problems with a dynamic probability distribution but static conditional independences. Our second approach focuses on the learning of k-LP-trees from sold items examples. We formally define the problem and introduce a score and a distance adapted to it. Our theoretical results include a learning algorithm of k-LP-trees with a convergence property, a linear LP-tree algorithm minimising the score we defined and a sample complexity result: a number of examples logarithmic in the number of attributes is enough to learn a "good" linear LP-tree. We finally present an experimental contribution that evaluates different languages whose models are learnt from a car sales history. The models learnt are used to recommend values in interactive configuration of Renault cars. The interactive configuration is a process in which the user chooses a value, one attribute at a time. The recommendation precision (the proportion of recommendations that would have been accepted by the user) and the recommendation time are measured. Besides, the parameters that influence the recommendation quality are investigated. Our results are promising: these methods, described either in the literature or in our contributions, are fast enough for an on-line use and their success rate is high, even close to the theoretical maximum.
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Pierre-François Gimenez. Apprentissage de préférences en espace combinatoire et application à la recommandation en configuration interactive. Intelligence artificielle [cs.AI]. Université Paul Sabatier - Toulouse III, 2018. Français. ⟨NNT : 2018TOU30182⟩. ⟨tel-02303275⟩

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