Designing Ontology-Driven Recommender Systems for Tourism

Ferraro, Pierluca; Re, Giuseppe Lo

doi:10.1007/978-3-319-03992-3_24

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…The authors matched tourists' profiles with characteristics of tourism objects through vector space where each ISSN: 2252-8938 1062 dimension is a tourist factor. In another approach, P. Ferraro and L. R. Giuseppe [23] proposed an architecture of a semantically adaptive recommender system assisting users in the travel planning phase and in on-site phase. Hybrid method of tourism recommender was also introduced to the literature in the research of Yan Chu et al [24].…”

Section: Ontologies For Tourism Industrymentioning

confidence: 99%

A novel ontology framework supporting model-based tourism recommender

Dung

Tho

et al. 2021

IJ-AI

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<span lang="EN-US">In this paper, we present a tourism recommender framework based on the cooperation of ontological knowledge base and supervised learning models. Specifically, a new tourism ontology, which not only captures domain knowledge but also specifies knowledge entities in numerical vector space, is presented. The recommendation making process enables machine learning models to work directly with the ontological knowledge base from training step to deployment step. This knowledge base can work well with classification models (e.g., k-nearest neighbours, support vector machines, or naıve bayes). A prototype of the framework is developed and experimental results confirm the feasibility of the proposed framework.</span>

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Section: Ontologies For Tourism Industrymentioning

confidence: 99%

A novel ontology framework supporting model-based tourism recommender

Dung

Tho

et al. 2021

IJ-AI

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“…Generally, PoIs have a semantic tag which refers to the intended functionality and the underlying activity that is to be performed, such as park, school, library [1], or associated to the type of available resources [43]. For this reason, trajectories between PoIs can be interpreted in two ways: as a sequence of integers representing the identifiers of the PoIs, for example

P o I 32 \to P o I 54

; or as a sequence of the semantic tags associated with the PoIs, for instance

L i b r a r y \to P a r k

.…”

Section: Multi‐agent Recommendation Systemmentioning

confidence: 99%

A multi‐agent system for itinerary suggestion in smart environments

Paola

Gaglio

Giammanco

et al. 2021

CAAI Trans on Intel Tech

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Modern smart environments pose several challenges, among which the design of intelligent algorithms aimed to assist the users. When a variety of points of interest are available, for instance, trajectory recommendations are needed to suggest users the most suitable itineraries based on their interests and contextual constraints. Unfortunately, in many cases, these interests must be explicitly requested and their lack causes the so-called cold-start problem. Moreover, lengthy travelling distances and excessive crowdedness of specific points of interest make itinerary planning more difficult. To address these aspects, a multi-agent itinerary suggestion system that aims at assisting the users in an online and collaborative way is proposed. A profiling agent is responsible for the detection of groups of users whose movements are characterised by similar semantic, spatial and temporal features; then, a recommendation agent leverages contextual information and dynamically associates the current user with the trajectory clusters according to a Multi-Armed Bandit policy. Framing the trajectory recommendation as a reinforcement learning problem permits to provide high-quality suggestions while avoiding both cold-start and preference elicitation issues. The effectiveness of the approach is demonstrated by some deployments in real-life scenarios, such as smart campuses and theme parks.

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“…The results proofed that the system was effective and flexible in planning a trip. Ferraro and Re [187] developed an ontology-driven adaptive recommender system. They used semantic information to assist users in travel planning.…”

Section: ) Ontology-based Recommendationsmentioning

confidence: 99%

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Yochum

Chang

et al. 2020

IEEE Access

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Linked open data is a relatively new topic area with great potential in a wide range of fields. In the tourism domain, many studies are using linked open data to address the problem of location-based recommendation by integrating data with other linked open datasets to enrich data and tourism content for reacting to the needs of tourists. This work aims not only to present a systematic review and mapping of the linked open data in location-based recommendation system on tourism domain, but also to provide an overview of the current research status in the area. First, we classify journal papers in this area from 2001 to 2018 by the year of publication. Second, we analyze and categorize journal papers by the different recommendation applications including problem formulations, data collections, proposed algorithms/systems, and experimental results. Third, we group the linked open data sources used in location-based recommendation system on tourism. Next, we summarize the research achievements and present the distribution of the different categories of location-based recommendation applications via linked open data. Last, we also guide the possible future research direction for the linked open data in location-based recommendations on tourism.

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Designing Ontology-Driven Recommender Systems for Tourism

Cited by 10 publications

References 18 publications

A novel ontology framework supporting model-based tourism recommender

A novel ontology framework supporting model-based tourism recommender

A multi‐agent system for itinerary suggestion in smart environments

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

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