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.
- Charu C. Aggarwal. 2016. Content-based recommender systems. In Recommender Systems. Springer, 139--166.Google ScholarDigital Library
- Charu C. Aggarwal. 2016. Recommender Systems. Springer. 225--254 pages.Google Scholar
- Òscar Celma. 2010. Music recommendation. In Music Recommendation and Discovery. Springer, 43--85.Google Scholar
- Òscar Celma and Pedro Cano. 2008. From hits to niches' or how popular artists' can bias music recommendation and discovery. In Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition. ACM, 5. Google ScholarDigital Library
- Paolo Cremonesi, Mehdi Elahi, and Yashar Deldjoo. 2016. Enhanced content based multimedia recommendation method. (09 2016). http://www.polimi.it/index.php?id=6247&sel_brevetto=5093 US Patent15/277490.Google Scholar
- Paolo Cremonesi, Mehdi Elahi, and Franca Garzotto. 2016. User interface patterns in recommendation-empowered content intensive multimedia applications. Springer Multimedia Tools and Applications (2016), 1--35. Google ScholarDigital Library
- Paolo Cremonesi, Franca Garzotto, and Roberto Turrin. 2013. User-centric vs. system-centric evaluation of recommender systems. In IFIP Conference on Human-Computer Interaction. Springer, 334--351.Google ScholarCross Ref
- Marco de Gemmis, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, and Giovanni Semeraro. 2015. Semantics-Aware Content-Based Recommender Systems. In Recommender Systems Handbook. Springer, 119--159.Google ScholarDigital Library
- Yashar Deldjoo, Paolo Cremonesi, Markus Schedl, and Massimo Quadrana. 2017. The effect of different video summarization models on the quality of video recommendation based on low-level visual. In Content-Based Multimedia Indexing (CBMI), 2017 15th International Workshop on. ACM.Google ScholarDigital Library
- Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi, Franca Garzotto, and Pietro Piazzolla. 2016. Recommending movies based on mise-en-scene design. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 1540--1547. Google ScholarDigital Library
- Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi, Franca Garzotto, Pietro Piazzolla, and Massimo Quadrana. 2016. Content-based Video Recommendation System based on Stylistic Visual Features. Journal on Data Semantics Special Issue on Recommender Systems (2016).Google Scholar
- Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi, Farshad Bakhshandegan Moghaddam, and Andrea Luigi Edoardo Caielli. 2016. How to Combine Visual Features with Tags to Improve Movie Recommendation Accuracy? In International Conference on Electronic Commerce and Web Technologies. Springer, 34--45.Google Scholar
- Yashar Deldjoo, Massimo Quadrana, Mehdi Elahi, and Paolo Cremonesi. 2017. Using Mise-En-Scene Visual Features based on MPEG7 and Deep Learning for Movie Recommendation. arXiv preprint arXiv:1704.06109 (2017).Google Scholar
- Chitra Dorai and Svetha Venkatesh. 2003. Bridging the semantic gap with computational media aesthetics. IEEE multimedia 10, 2 (2003), 15--17. Google ScholarDigital Library
- Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys '14). ACM, New York, NY, USA, 161--168. Google ScholarDigital Library
- Daniel Fleder and Kartik Hosanagar. 2009. Blockbuster culture's next rise or fall: The impact of recommender systems on sales diversity. Management science 55, 5 (2009), 697--712. Google ScholarDigital Library
- Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. 2010. Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Recommender systems. ACM, 257-- 260. Google ScholarDigital Library
- Asela Gunawardana and Guy Shani. 2015. Evaluating recommender systems. In Recommender Systems Handbook. Springer, 265--308.Google Scholar
- Ruining He, Chen Fang, Zhaowen Wang, and Julian McAuley. 2016. Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). ACM, New York, NY, USA, 309--316. Google ScholarDigital Library
- Lu Jiang, Shoou-I Yu, Deyu Meng, Teruko Mitamura, and Alexander G. Hauptmann. 2015. Bridging the ultimate semantic gap: A semantic search engine for internet videos. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. ACM, 27--34. Google ScholarDigital Library
- Yannis Kalantidis, Lyndon Kennedy, and Li-Jia Li. 2013. Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos. In Proceedings of the 3rd ACM conference on International conference on multimedia retrieval. ACM, 105--112. Google ScholarDigital Library
- Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction 22, 4-5 (2012), 441--504. Google ScholarDigital Library
- Joseph A. Konstan and John Riedl. 2012. Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction 22, 1-2 (2012), 101--123. Google ScholarDigital Library
- Hao Ma, Jianke Zhu, Michael Rung-Tsong Lyu, and Irwin King. 2010. Bridging the semantic gap between image contents and tags. IEEE Transactions on Multimedia 12, 5 (2010), 462--473. Google ScholarDigital Library
- Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. Being accurate is not enough: how accuracy metrics have hurt recommender systems. In CHI'06 extended abstracts on Human factors in computing systems. ACM, 1097--1101. Google ScholarDigital Library
- Pablo Messina, Vicente Dominquez, Denis Parra, Christoph Trattner, and Alvaro Soto. 2017. Exploring Content-based Artwork Recommendation with Metadata and Visual Features. (2017).Google Scholar
- Pearl Pu, Li Chen, and Rong Hu. 2011. A user-centric evaluation framework for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems. ACM, 157--164. Google ScholarDigital Library
- Zeeshan Rasheed, Yaser Sheikh, and Mubarak Shah. 2005. On the use of computable features for film classification. Circuits and Systems for Video Technology, IEEE Transactions on 15, 1 (2005), 52--64. Google ScholarDigital Library
- Sujoy Roy and Sharath Chandra Guntuku. 2016. Latent Factor Representations for Cold-Start Video Recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). ACM, New York, NY, USA, 99--106. Google ScholarDigital Library
- David J. Sheskin. 2003. Handbook of parametric and nonparametric statistical procedures. crc Press. Google ScholarDigital Library
- Mi Zhang and Neil Hurly. 2009. Evaluating the diversity of top-n recommendations. In Tools with Artificial Intelligence, 2009. ICTAI'09. 21st International Conference on. IEEE, 457--460. Google ScholarDigital Library
Index Terms
- Exploring the Semantic Gap for Movie Recommendations
Recommendations
Personality, movie preferences, and recommendations
ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningPersonality is an important factor that influences people's decisions, actions, and tastes. While previous research has used surveys to establish a connection between personality and media preferences, to date there has been no research that connects ...
Making personalized movie recommendations for children
iiWAS '16: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and ServicesMultimedia have significant impact on the social and psychological development of children who are often explored to inappropriate materials, including movies that are either accessible online or through other multimedia channels. Even though not all ...
Effects of anchoring process under preference stabilities for interactive movie recommendations
This study explores how the stability of users' preferences influences recommendation results and how this stability relates to the effectiveness of developing recommendation strategies. In this work, we propose an anchor-based hybrid filtering ...
Comments