Semantic Graph-Based Recommender System. Application in Cultural Heritage

2019 ◽  
pp. 109-121
Author(s):  
Sara Qassimi ◽  
El Hassan Abdelwahed
Keyword(s):  
Cultural Heritage ◽  
Recommender System ◽  
System Application ◽  
Semantic Graph

scholarly journals Towards a Semantic Graph-based Recommender System. A Case Study of Cultural Heritage

2021 ◽  
Vol 27 (7) ◽  
pp. 714-733
Author(s):  
Sara Qassimi ◽  
El Hassan Abdelwahed
Keyword(s):  
Cultural Heritage ◽  
Recommender System ◽  
Information Overload ◽  
Semantic Relatedness ◽  
Collaborative Tagging ◽  
Shared Resources ◽  
Semantic Graph ◽  
System A ◽  
The Social ◽  
Heritage Area

Research on digital cultural heritage has raised the importance of providing visitors with relevant assistance before and during their visits. With the advent of the social web, the cultural heritage area is affected by the problem of information overload. Indeed, a large number of available resources have emerged coming from the social information systems (SocIS). Therefore, visitors are swamped with enormous choices in their visited cities. SocIS platforms use the features of collaborative tagging, named folksonomy, to commonly contribute to the management of the shared resources. However, collaborative tagging uses uncontrolled vocabulary which semanti- cally weakens the description of resources, consequently decreases their classification, clustering, thereby their recommendation. Therefore, the shared resources have to be pertinently described to ameliorate their recommendations. In this paper, we aim to enhance the cultural heritage visits by suggesting semantically related places that are most likely to interest a visitor. Our proposed approach represents a semantic graph-based recommender system of cultural heritage places through two steps; (1) constructing an emergent semantic description that semantically augments the place and (2) effectively modeling the emerging graphs representing the semantic relatedness of similar cultural heritage places and their related tags. The experimental evaluation shows relevant results attesting the efficiency of the proposed approach.

scholarly journals Social knowledge-based recommender system. Application to the movies domain

2012 ◽  
Vol 39 (12) ◽  
pp. 10990-11000 ◽  
Author(s):  
Walter Carrer-Neto ◽  
María Luisa Hernández-Alcaraz ◽  
Rafael Valencia-García ◽  
Francisco García-Sánchez
Keyword(s):  
Recommender System ◽  
System Application ◽  
Social Knowledge ◽  
Knowledge Based

A Content-Based Recommender System for Hidden Cultural Heritage Sites Enhancing

2021 ◽  
pp. 97-109
Author(s):  
Mario Casillo ◽  
Dajana Conte ◽  
Marco Lombardi ◽  
Domenico Santaniello ◽  
Alfredo Troiano ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Recommender System ◽  
Heritage Sites

Integrating a Content-Based Recommender System into Digital Libraries for Cultural Heritage

2010 ◽  
pp. 27-38 ◽  
Author(s):  
Cataldo Musto ◽  
Fedelucio Narducci ◽  
Pasquale Lops ◽  
Marco de Gemmis ◽  
Giovanni Semeraro
Keyword(s):  
Cultural Heritage ◽  
Recommender System ◽  
Digital Libraries

scholarly journals A Multimedia Semantic Recommender System for Cultural Heritage Applications

2011 ◽  
Author(s):  
Massimiliano Albanese ◽  
Antonio d'Acierno ◽  
Vincenzo Moscato ◽  
Fabio Persia ◽  
Antonio Picariello
Keyword(s):  
Cultural Heritage ◽  
Recommender System

A novel user review-based contextual recommender system

2021 ◽  
Author(s):  
N. Zafar Ali Khan ◽  
R. Mahalakshmi
Keyword(s):  
Recommender System ◽  
System Application ◽  
Rating Data ◽  
Knowledge Mining ◽  
Structure Space ◽  
Planning Framework ◽  
Points Of View ◽  
Logical Data ◽  
User Review

Recommendation systems are shrewd applications for knowledge mining that profoundly handle the problem of data overload. Various literature explores different philosophies to create ideas and recommends different strategies according to the needs of customers. Most of the work in the suggested structure space focuses on extending the accuracy of the recommendation by using a few possible methods where the principle purpose remains to improve the accuracy of suggestions while avoiding other plan objectives, such as the particular situation of a client. By using appropriate customer rating data, the biggest test for a suggested system is to generate substantial proposals. A setting is an enormous concept that can think of numerous points of view: for example, the community of friends of a client, time, mindset, environment, organization, type of day, classification of an item, description of the object, place, and language. The rating behavior of customers typically varies in different environments. We have proposed a new review-based contextual recommender (RBCR) system application from this line of analysis, in particular a novel recommender system, which is an adaptable, quick, and accurate piece planning framework that perceives the significance of setting and fuses the logical data using piece stunt while making expectations. We have contrasted our suggested calculation with pre- and post-sifting methods as they have been the most common methodologies in writing to illuminate the issue of setting conscious suggestion. Our studies show that considering the logical data, the display of a system will increase and provide better, appropriate and important results on various evaluation measurements.

An Edge Intelligence Empowered Recommender System Enabling Cultural Heritage Applications

2019 ◽  
Vol 15 (7) ◽  
pp. 4266-4275 ◽  
Author(s):  
Xin Su ◽  
Giancarlo Sperli ◽  
Vincenzo Moscato ◽  
Antonio Picariello ◽  
Christian Esposito ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Recommender System

A Hybrid Recommender System for Cultural Heritage Promotion

2021 ◽  
Author(s):  
Vincenzo Agate ◽  
Federico Concone ◽  
Salvatore Gaglio ◽  
Andrea Giammanco
Keyword(s):  
Cultural Heritage ◽  
Recommender System ◽  
Hybrid Recommender System ◽  
Hybrid Recommender
Sign in / Sign up

Export Citation Format

Share Document