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Scalable models for points-of-interest recommender systems

Abstract : The task of points-of-interest (POI) recommendations has become an essential feature in location-based social networks. However it remains a challenging problem because of specific constraints of these networks. In this thesis I investigate new approaches to solve the personalized POI recommendation problem. Three main contributions are proposed in this work. The first contribution is a new matrix factorization model that integrates geographical and temporal influences. This model is based on a specific processing of geographical data. The second contribution is an innovative solution against the implicit feedback problem. This problem corresponds to the difficulty to distinguish among unvisited POI the actual "unknown" from the "negative" ones. Finally the third contribution of this thesis is a new method to generate recommendations with large-scale datasets. In this approach I propose to combine a new geographical clustering algorithm with users’ implicit social influences in order to define local and global mobility scales.
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Submitted on : Tuesday, April 27, 2021 - 4:56:07 PM
Last modification on : Thursday, April 29, 2021 - 3:06:32 AM


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  • HAL Id : tel-02085091, version 2


Jean-Benoît Griesner. Scalable models for points-of-interest recommender systems. Information Retrieval [cs.IR]. Télécom ParisTech, 2018. English. ⟨NNT : 2018ENST0037⟩. ⟨tel-02085091v2⟩



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