In this paper we propose a method for video recommendation in Social Networks based on crowdsourced and automatic video annotations of salient frames. We show how two human factors, users' self-expression in user profiles and perception of visual saliency in videos, can be exploited in order to stimulate annotations and to obtain an efficient representation of video content features. Results are assessed through experiments conducted on a prototype of social network for video sharing. Several baseline approaches are evaluated and we show how the proposed method improves over them.
Item-Based video recommendation: An hybrid approach considering human factors / Ferracani, Andrea; Pezzatini, Daniele; Bertini, Marco; Del Bimbo, Alberto. - STAMPA. - (2016), pp. 351-354. (Intervento presentato al convegno 6th ACM International Conference on Multimedia Retrieval, ICMR 2016 tenutosi a usa nel 2016) [10.1145/2911996.2912066].
Item-Based video recommendation: An hybrid approach considering human factors
FERRACANI, ANDREA;PEZZATINI, DANIELE;BERTINI, MARCO;DEL BIMBO, ALBERTO
2016
Abstract
In this paper we propose a method for video recommendation in Social Networks based on crowdsourced and automatic video annotations of salient frames. We show how two human factors, users' self-expression in user profiles and perception of visual saliency in videos, can be exploited in order to stimulate annotations and to obtain an efficient representation of video content features. Results are assessed through experiments conducted on a prototype of social network for video sharing. Several baseline approaches are evaluated and we show how the proposed method improves over them.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.