Paper Info
Title
Exploring the Semantic Gap for Movie Recommendations
Abstract
In the last years, there has been much attention given to the semantic gap problem in multimedia retrieval systems. Much effort has been devoted to bridge this gap by building tools for the extraction of high-level, semantics-based features from multimedia content, as low-level features are not considered useful because they deal primarily with representing the perceived content rather than the semantics of it. In this paper, we explore a different point of view by leveraging the gap between low-level and high-level features. We experiment with a recent approach for movie recommendation that extract low-level Mise-en-Scéne features from multimedia content and combine it with high-level features provided by the wisdom of the crowd. To this end, we first performed an offline performance assessment by implementing a pure content-based recommender system with three different versions of the same algorithm, respectively based on (i) conventional movie attributes, (ii) mise-en-scene features, and (iii) a hybrid method that interleaves recommendations based on movie attributes and mise-en-scene features. In a second study, we designed an empirical study involving 100 subjects and collected data regarding the quality perceived by the users. Results from both studies show that the introduction of mise-en-scéne features in conjunction with traditional movie attributes improves both offline and online quality of recommendations.
Year
DOI
Venue
2017
10.1145/3109859.3109908
RecSys
Field
DocType
ISBN
Recommender system,Data mining,Wisdom of the crowd,Computer science,Semantic gap,Empirical research,Semantics,Mise en scène
Conference
978-1-4503-4652-8
Citations 
PageRank 
References 
12
0.49
19
Authors
6
Name
Order
Citations
PageRank
Mehdi Elahi140829.41
Yashar Deldjoo218624.74
Farshad Bakhshandegan Moghaddam3234.35
Leonardo Cella4161.23
Stefano Cereda5242.72
Paolo Cremonesi6130687.23