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Context-aware and Ontology-based Recommender System for E-tourism

Abstract : Frequently, travelers try to collect information for planing a trip or when being at the destination. Usually, tourists depend on places' reviews to make the choice, but this implies prior knowledge of the touristic places and explicit search for suggestions through interaction with applications (i.e., PULL paradigm). In contrast, a PUSH approach, in which the application proactively triggers a recommendation process according to users' preferences and when necessary,-seems to be a more reasonable solution. Recommender systems have become appropriate applications to help tourists in their trip planning. However, they still have limitations, such as poor consideration of users' profiles and their contexts, their predictable suggestions, and the lack of a standard representation of the knowledge managed. We propose a user-centric recommender system architecture, that supports both PULL and PUSH approaches, assisted by an ontology-based spreading activation algorithm for context-aware recommendations, with a focus on decreasing predictable outputs and increasing serendipity, based on an aging-like approach. To demonstrate its suitability and performance, we develop a first prototype of the architecture and simulate different scenarios, varying users' profiles, preferences, and context parameters. Results show that the ontology-based spreading activation and the proposed aging system provide relevant and varied recommendations according to users' preferences, while considering their context and improving the serendipity of the system when comparing with a state-of-the-art work.
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https://hal-univ-pau.archives-ouvertes.fr/hal-03346669
Contributor : Philippe Roose Connect in order to contact the contributor
Submitted on : Thursday, September 16, 2021 - 3:07:05 PM
Last modification on : Friday, September 17, 2021 - 3:34:29 AM

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  • HAL Id : hal-03346669, version 1

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Gustavo Castellanos, Yudith Cardinale, Philippe Roose. Context-aware and Ontology-based Recommender System for E-tourism. ICSOFT, Jun 2021, Lisbon, Portugal. ⟨hal-03346669⟩

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