A Novel Strategy for Recommending Multimedia Objects and its Application in the Cultural Heritage Domain

2011 ◽  
Vol 2 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Massimiliano Albanese ◽  
Antonio d’Acierno ◽  
Vincenzo Moscato ◽  
Fabio Persia ◽  
Antonio Picariello
Keyword(s):  
Cultural Heritage ◽  
Recommender System ◽  
Digital Libraries ◽  
Social Behaviour ◽  
Information Overload ◽  
Information Access ◽  
Experimental Results ◽  
Low Level ◽  
Novel Strategy

One of the most important challenges in the information access field, especially for multimedia repositories, is information overload. To cope with this problem, in this paper, the authors present a strategy for a recommender system that computes customized recommendations for users’ accessing multimedia collections, using semantic contents and low-level features of multimedia objects, past behaviour of individual users, and social behaviour of the users’ community as a whole. The authors implement their strategy in a recommender prototype for browsing image digital libraries in the Cultural Heritage domain. They then investigate the effectiveness of the proposed approach, based on the users’ satisfaction. The preliminary experimental results show that the approach is promising and encourages further research in this direction.

A Novel Strategy for Recommending Multimedia Objects and its Application in the Cultural Heritage Domain

2013 ◽  
pp. 274-290
Author(s):  
Massimiliano Albanese ◽  
Antonio d’Acierno ◽  
Vincenzo Moscato ◽  
Fabio Persia ◽  
Antonio Picariello
Keyword(s):  
Cultural Heritage ◽  
Recommender System ◽  
Digital Libraries ◽  
Social Behaviour ◽  
Information Overload ◽  
Information Access ◽  
Experimental Results ◽  
Low Level ◽  
Novel Strategy

One of the most important challenges in the information access field, especially for multimedia repositories, is information overload. To cope with this problem, in this paper, the authors present a strategy for a recommender system that computes customized recommendations for users’ accessing multimedia collections, using semantic contents and low-level features of multimedia objects, past behaviour of individual users, and social behaviour of the users’ community as a whole. The authors implement their strategy in a recommender prototype for browsing image digital libraries in the Cultural Heritage domain. They then investigate the effectiveness of the proposed approach, based on the users’ satisfaction. The preliminary experimental results show that the approach is promising and encourages further research in this direction.

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 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 How the Multiplicity of Suggested Information Affects the Behavior of a User in a Recommender System

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 741
Author(s):  
Yuseok Ban ◽  
Kyungjae Lee
Keyword(s):  
High Precision ◽  
Recommender System ◽  
Visual Information ◽  
Experimental Results ◽  
Supplementary Information ◽  
Digital Platform ◽  
Different Types ◽  
Types Of Information

Many researchers have suggested improving the retention of a user in the digital platform using a recommender system. Recent studies show that there are many potential ways to assist users to find interesting items, other than high-precision rating predictions. In this paper, we study how the diverse types of information suggested to a user can influence their behavior. The types have been divided into visual information, evaluative information, categorial information, and narrational information. Based on our experimental results, we analyze how different types of supplementary information affect the performance of a recommender in terms of encouraging users to click more items or spend more time in the digital platform.

scholarly journals A MODEL BASED ON FUZZY LINGUISTIC INFORMATION TO EVALUATE THE QUALITY OF DIGITAL LIBRARIES

2010 ◽  
Vol 09 (03) ◽  
pp. 455-472 ◽  
Author(s):  
F. J. CABRERIZO ◽  
J. LÓPEZ-GIJÓN ◽  
A. A. RUÍZ ◽  
E. HERRERA-VIEDMA
Keyword(s):  
Digital Libraries ◽  
User Satisfaction ◽  
Information Access ◽  
Great Influence ◽  
Linguistic Information ◽  
Model Based ◽  
Global Quality ◽  
Fuzzy Linguistic ◽  
The Web

The Web is changing the information access processes and it is one of the most important information media. Thus, the developments on the Web are having a great influence over the developments on others information access instruments as digital libraries. As the development of digital libraries is to satisfy user need, user satisfaction is essential for the success of a digital library. The aim of this paper is to present a model based on fuzzy linguistic information to evaluate the quality of digital libraries. The quality evaluation of digital libraries is defined using users' perceptions on the quality of digital services provided through their Websites. We assume a fuzzy linguistic modeling to represent the users' perception and apply automatic tools of fuzzy computing with words based on the LOWA and LWA operators to compute global quality evaluations of digital libraries. Additionally, we show an example of application of this model where three Spanish academic digital libraries are evaluated by fifty users.

scholarly journals A Two-Level Sound Classification Platform for Environmental Monitoring

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Stelios A. Mitilineos ◽  
Stelios M. Potirakis ◽  
Nicolas-Alexander Tatlas ◽  
Maria Rangoussi
Keyword(s):  
Cultural Heritage ◽  
Organizational Innovation ◽  
Gaussian Mixture ◽  
Low Level ◽  
Heritage Sites ◽  
Acoustic Sensor Networks ◽  
Preventive Actions ◽  
Integrated Platform ◽  
High Level ◽  
The Impact

STORM is an ongoing European research project that aims at developing an integrated platform for monitoring, protecting, and managing cultural heritage sites through technical and organizational innovation. Part of the scheduled preventive actions for the protection of cultural heritage is the development of wireless acoustic sensor networks (WASNs) that will be used for assessing the impact of human-generated activities as well as for monitoring potentially hazardous environmental phenomena. Collected sound samples will be forwarded to a central server where they will be automatically classified in a hierarchical manner; anthropogenic and environmental activity will be monitored, and stakeholders will be alarmed in the case of potential malevolent behavior or natural phenomena like excess rainfall, fire, gale, high tides, and waves. Herein, we present an integrated platform that includes sound sample denoising using wavelets, feature extraction from sound samples, Gaussian mixture modeling of these features, and a powerful two-layer neural network for automatic classification. We contribute to previous work by extending the proposed classification platform to perform low-level classification too, i.e., classify sounds to further subclasses that include airplane, car, and pistol sounds for the anthropogenic sound class; bird, dog, and snake sounds for the biophysical sound class; and fire, waterfall, and gale for the geophysical sound class. Classification results exhibit outstanding classification accuracy in both high-level and low-level classification thus demonstrating the feasibility of the proposed approach.

scholarly journals Enhanced Hybrid Recommender System using Social Friend Network

2014 ◽  
Vol 10 (4) ◽  
pp. 2023-2031
Author(s):  
Shalmali A. Patil ◽  
Reena Pagare
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Recommender System ◽  
Social Relationship ◽  
Information Overload ◽  
Great Success ◽  
Traditional System ◽  
Use Of Data ◽  
The Social ◽  
Hybrid Recommender

Lots of people employ recommender systems to diminish the information overload over the internet. This leads the user in a personalized manner to hit upon interesting or helpful objects in a huge space of possible options. Amongst different techniques, Collaborative filtering recommender system has pulled off great success. But this technique pays no heed towards the social relationship of the users. This problem gave birth to the Social recommender system technology which possesses the capability to recognize users likings and preferences and their social relationships. In this paper, we present novel method where we combine collaborative filtering recommender system with social friend network to use social relationships. For this, we have made use of data related to users which provides their interests as well as their social relationship. Our method helps to find the friends with dissimilar tastes and determine the close friends amongst direct friends of targeted user which has more similar tastes. This proposed approach resulted in more precise and realistic results than traditional system.

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