Podrobný výpis o publikaci

GE, Mouzhi a Fabio PERSIA. A Generalized Evaluation Framework for Multimedia Recommender Systems. International Journal of Semantic Computing. World Scientific, 2018, roč. 12, č. 4, s. 541-557. ISSN 1793-351X. Dostupné z: https://dx.doi.org/10.1142/S1793351X18500046.

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Základní údaje
Originální název	A Generalized Evaluation Framework for Multimedia Recommender Systems
Autoři	GE, Mouzhi (156 Čína, garant, domácí) a Fabio PERSIA (380 Itálie).
Vydání	International Journal of Semantic Computing, World Scientific, 2018, 1793-351X.

Další údaje
Originální jazyk	angličtina
Typ výsledku	Článek v odborném periodiku
Obor	10201 Computer sciences, information science, bioinformatics
Stát vydavatele	Spojené státy
Utajení	není předmětem státního či obchodního tajemství
Kód RIV	RIV/00216224:14330/18:00103876
Organizační jednotka	Fakulta informatiky
Doi	http://dx.doi.org/10.1142/S1793351X18500046
UT WoS	000453524500005
Klíčová slova anglicky	Multimedia recommender system; multimedia recommendation; evaluation framework; evaluation criteria
Příznaky	Mezinárodní význam, Recenzováno
Změnil	Změnil: RNDr. Pavel Šmerk, Ph.D., učo 3880. Změněno: 29. 4. 2019 17:24.

Anotace
With the widespread availability of media technologies, such as real-time streaming, new Internet-of-Thing devices and smart phones, multimedia data are extensively increased and the big multimedia data rapidly spread over various social networks. This has created complexity and information overload for users to choose the suitable multimedia objects. Thus, different multimedia recommender systems have been emerging to help users find the useful multimedia objects that are possibly preferred by the user. However, the evaluation of these multimedia recommender systems is still in an ad-hoc stage. Given the distinct features of multimedia objects, the evaluation criteria adopted from the general recommender systems might not be effectively used to evaluate multimedia recommendations. In this paper, we therefore review and analyze the evaluation criteria that have been used in the previous multimedia recommender system papers. Based on the review, we propose a generalized evaluation framework to guide the researchers and practitioners to perform evaluations, especially user-centric evaluations, for multimedia recommender systems.

Anotace

With the widespread availability of media technologies, such as real-time streaming, new Internet-of-Thing devices and smart phones, multimedia data are extensively increased and the big multimedia data rapidly spread over various social networks. This has created complexity and information overload for users to choose the suitable multimedia objects. Thus, different multimedia recommender systems have been emerging to help users find the useful multimedia objects that are possibly preferred by the user. However, the evaluation of these multimedia recommender systems is still in an ad-hoc stage. Given the distinct features of multimedia objects, the evaluation criteria adopted from the general recommender systems might not be effectively used to evaluate multimedia recommendations. In this paper, we therefore review and analyze the evaluation criteria that have been used in the previous multimedia recommender system papers. Based on the review, we propose a generalized evaluation framework to guide the researchers and practitioners to perform evaluations, especially user-centric evaluations, for multimedia recommender systems.