Detailed Information on Publication Record
2018
A Generalized Evaluation Framework for Multimedia Recommender Systems
GE, Mouzhi and Fabio PERSIABasic information
Original name
A Generalized Evaluation Framework for Multimedia Recommender Systems
Authors
GE, Mouzhi (156 China, guarantor, belonging to the institution) and Fabio PERSIA (380 Italy)
Edition
International Journal of Semantic Computing, World Scientific, 2018, 1793-351X
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
RIV identification code
RIV/00216224:14330/18:00103876
Organization unit
Faculty of Informatics
UT WoS
000453524500005
Keywords in English
Multimedia recommender system; multimedia recommendation; evaluation framework; evaluation criteria
Tags
International impact, Reviewed
Změněno: 29/4/2019 17:24, RNDr. Pavel Šmerk, Ph.D.
Abstract
V originále
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.