research-article

Making personalized movie recommendations for children

Authors:

Humphrey Leung,

Yiu-Kai NgAuthors Info & Claims

iiWAS '16: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services

Pages 96 - 105

https://doi.org/10.1145/3011141.3011142

Published: 28 November 2016 Publication History

Abstract

Multimedia 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 movies are bad, there are negative effects of offensive languages, violence, and sexuality as exhibited in movies. Parents and guidance of children need all the help they can get to promote the healthy use of movies these days. To offer them appropriate movies of interest to their youths, we have developed MovReC, a personalized movie recommender for children, which is designed to provide educational and suitable entertaining opportunities for children. Unlike Amazon and other online movie recommendation systems, such as Common Sense Media, IMDb, and TasteKid, MovReC is unique, since to the best of our knowledge MovReC is the first personalized children movie recommender. Moreover, MovReC determines the appealingness of a movie for a particular user based on its children-appropriate score computed by using the Backpropagation model, pre-defined category using LDA, its predicted rating using Matrix Factorization, and sentiments based on its users' reviews, which along with its like/dislike count and genres, yield the features considered by MovReC. MovReC combines these features by using the CombMNZ model to rank and recommend movies. The performance evaluation of MovReC clearly demonstrates its effectiveness and its recommended movies are highly regarded by its users.

References

[1]

K. Bacon. Movies, Books, and "The Giver". In Harvard Educational Magazine. Harvard Graduate School of Education, Winter edition, January 2015.

[2]

D. Blei, A. Ng, and M. Jordan. Latent Dirichlet Allocation. Machine Learning Research, 3:993--1022, 2003.

Digital Library

[3]

S. Choi, S. Ko, and Y. Han. A Movie Recommendation Algorithm Based on Genre Correlations. Expert Systems with Applications, 39(9):8079--8085, 2012.

Digital Library

[4]

K. Clarke-Stewart and R. Beck. Maternal Scaffolding and Children's Narrative Retelling of a Movie Story. Early Childhood Research Quarterly, 14(3):409--434, 1999.

[5]

Q. Diao, M. Qiu, C. Wu, A. Smola, J. Jiang, and C. Wang. Jointly Modeling Aspects, Ratings and Sentiments for Movie Recommendation. In Proceedings of the 20th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), pages 193--202, 2014.

Digital Library

[6]

Film Ratings. Motion Picture Associate of America Film Ratings. MPAA.

[7]

J. Funk, H. Baldacci, T. Pasold, and J. Baumgardner. Violence Exposure in Real-Life, Video Games, Television, Movies, and the Internet: Is There Desensitization? Adolescence, 27(1):23--39, 2004.

[8]

T. Griffiths and M. Steyvers. Finding Scientific Topics. Proceedings of the National Academy of Sciences (PNAS), 101(1):5228--5235, 2004.

[9]

A. Harding. Violent Video Games Linked to Child Aggression. CNN.com, 2008.

[10]

F. Harper and J. Konstan. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS), 5(4):Article 19, December 2015.

Digital Library

[11]

K. Järvelin and J. Kekäläinen. Cumulated Gain-Based Evaluation of IR Techniques. ACM Transactions on Information Systems (TOIS), 20(4):422--446, 2002.

Digital Library

[12]

H. Jenkins. Transmedia 202: Further Reflections. Confessions of an AcaFan. August 1, 2011.

[13]

J. Koberstein and Y.-K. Ng. Using Word Clusters to Detect Similar Web Documents. In Proceedings of the First International Conference on Knowledge Science, Engineering and Management, LNAI 4092, pages 215--228, 2006.

Digital Library

[14]

J. Lee. Analyses of Multiple Evidence Combination. In Proceedings of the International Conference on Research and Development in Information Retrieval (ACM SIGIR), pages 267--276, 1997.

Digital Library

[15]

A. Liu, Y. Zhang, and J. Li. Personalized Movie Recommendation. In Proceedings of the ACM International Conference on Multimedia, pages 845--848, 2009.

Digital Library

[16]

T. Mitchell. Machine Learning. McGraw Hill, 1997.

Digital Library

[17]

I. Pilászy and D. Tikk. Recommending New Movies: Even a Few Ratings are More Valuable than Metadata. In Proceedings of the 3^rd ACM Conference on Recommender Systems (ACM RecSys), pages 93--100, 2009.

Digital Library

[18]

M. Rich. Screen Time Study Finds Education Drop-Off. The New York Times. January 23, 2014.

[19]

L. Rosen. Teaching the iGeneration. Educational Leadership, 68(5):10--15, 2011.

[20]

Y. Shi, M. Larson, and A. Hanjalic. Mining Mood-Specific Movie Similarity with Matrix Factorization for Context-Aware Recommendation. In Proceedings of the Workshop on Context-Aware Movie Recommendation, pages 34--40, 2010.

Digital Library

[21]

B. Stelter. 8 Hours a Day Spent on Screens, Study Finds. The New York Times. March 26, 2009.

[22]

K. Thompson and F. Yokota. Violence, Sex, and Profanity in Films: Correlation of Movie Ratings with Content. Medscape General Medicine, 6(3):Article 3, 2004.

[23]

D. Ukpong, U. Uyanga, and I. Nyorere. Cartoon Movies and Nursery Schools Pupils' Development of Cognitive and Social Skills: Implications for Social Studies Teachers. American Journal of Social Sciences, 3(3):74--78, 2015.

[24]

J. Wakefield. Children Spend Six Hours or More Day on Screens. BBC News. March 27, 2015.

[25]

J. Yan, N.-Y. Xu, X.-F. Cai, R. Gao, Y. Wang, R. Luo, and F.-H. Hsu. An FPGA-Based Accelerator for LambdaRank in Web Search Engines. ACM Transactions on Reconfigurable Technology and Systems (TRETS), 4(3):Article 25, 2011.

Digital Library

[26]

X. Yang, Y. Guo, and Y. Liu. Bayesian-Inference-Based Recommendation in Online Social Networks. IEEE Transactions on Parallel and Distributed Systems, 24(4):642--651, April 2013.

Digital Library

[27]

Y. Zheng, H. Yao, Y. Zhang, and P. Xu. Age Classification Based on Back-Propagation Network. In Proceedings of the International Conference on Internet Multimedia Computing and Service (ICIMCS), pages 319--322, 2013.

Digital Library

Cited By

Jelodar HWang YXiao GRabbani MZhao RAyobi SHu PMasood I(2020)Recommendation system based on semantic scholar mining and topic modeling on conference publicationsSoft Computing10.1007/s00500-020-05397-3Online publication date: 3-Nov-2020
https://doi.org/10.1007/s00500-020-05397-3
Vimala SVivekanandan K(2019)A Kullback–Leibler divergence-based fuzzy C-means clustering for enhancing the potential of an movie recommendation systemSN Applied Sciences10.1007/s42452-019-0708-91:7Online publication date: 10-Jun-2019
https://doi.org/10.1007/s42452-019-0708-9

Index Terms

Making personalized movie recommendations for children
1. Information systems
  1. Information systems applications

Recommendations

Personalized movie recommendation
MM '09: Proceedings of the 17th ACM international conference on Multimedia

Facing the vast amount of novel production in the movie industry, people are in favor of choosing their favorite candidates quickly and previewing movie contents conveniently so as to decide whether they appeal to their personal taste. To meet this ...
Personalized Recommendation Algorithm Using User Demography Information
WKDD '09: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining

Personalized recommendation systems are web-based systems that aim at predicting a user’s interest on available products and services by relying on previously rated items and dealing with the problem of information and product overload. User demography ...
Attention-driven Factor Model for Explainable Personalized Recommendation
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

Latent Factor Models (LFMs) based on Collaborative Filtering (CF) have been widely applied in many recommendation systems, due to their good performance of prediction accuracy. In addition to users' ratings, auxiliary information such as item features ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

iiWAS '16: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services

November 2016

528 pages

ISBN:9781450348072

DOI:10.1145/3011141

General Chair:
Gabriele Anderst-Kotsis
Johannes Kepler University Linz, Austria

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 November 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

iiWAS '16

iiWAS '16: 18th International Conference on Information Integration and Web-based Applications and Services

November 28 - 30, 2016

Singapore, Singapore

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
277
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)1

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Jelodar HWang YXiao GRabbani MZhao RAyobi SHu PMasood I(2020)Recommendation system based on semantic scholar mining and topic modeling on conference publicationsSoft Computing10.1007/s00500-020-05397-3Online publication date: 3-Nov-2020
https://doi.org/10.1007/s00500-020-05397-3
Vimala SVivekanandan K(2019)A Kullback–Leibler divergence-based fuzzy C-means clustering for enhancing the potential of an movie recommendation systemSN Applied Sciences10.1007/s42452-019-0708-91:7Online publication date: 10-Jun-2019
https://doi.org/10.1007/s42452-019-0708-9

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents