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A new horizon for the recommendation: Integration of spatial dimensions to aid decision making

Abstract : Nowadays it is very common to represent a system in terms of relationships between objects. One of the common applications of such relational data is Recommender System (RS), which usually deals with the relationships between users and items. Probabilistic Relational Models (PRMs) can be a good choice for modeling probabilistic dependencies between such objects. A growing trend in recommender systems is to add spatial dimensions to these objects, and make recommendations considering the location of users and/or items. This thesis deals with the (not much explored) intersection of three related fields – Probabilistic Relational Models (a method to learn probabilistic models from relational data), spatial data (often used in relational settings), and recommender systems (which deal with relational data). The first contribution of this thesis deals with the overlapping of PRM and recommender systems. We have proposed a PRM-based personalized recommender system that is capable of making recommendations from user queries in cold-start systems without user profiles. Our second contribution addresses the problem of integrating spatial information into a PRM.
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Contributor : Rajani Chulyadyo Connect in order to contact the contributor
Submitted on : Sunday, December 25, 2016 - 1:41:50 PM
Last modification on : Friday, May 6, 2022 - 10:02:50 AM
Long-term archiving on: : Tuesday, March 21, 2017 - 9:37:11 AM


  • HAL Id : tel-01422348, version 1


Rajani Chulyadyo. A new horizon for the recommendation: Integration of spatial dimensions to aid decision making. Computer science. Université de Nantes, 2016. English. ⟨tel-01422348⟩



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