research-article

Movie SCoRe: Personalized Movie Recommendation on Mobile Devices

Authors:

Harris Papadakis,

Nikos Michalakis,

Paraskevi Fragopoulou,

Costas Panagiotakis,

Athanasios MalamosAuthors Info & Claims

PCI '17: Proceedings of the 21st Pan-Hellenic Conference on Informatics

Article No.: 46, Pages 1 - 6

https://doi.org/10.1145/3139367.3139383

Published: 28 September 2017 Publication History

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Abstract

Recommender systems try to predict the preferences of users for specific items, based on an analysis of previous consumer behaviour. In this paper, we present Movie SCoRe, a mobile device application for personalized movie recommendation, based on a novel recommendation algorithm. This easy-to-use application allows users to effortlessly specify their preferences by rating already watched movies. The application, in turn, employs the aforementioned state-of-the-art algorithm in order to provide the user with accurate, personalized movie recommendations. In this paper, we describe the design, implementation and functionality of the mobile-based application as well as the basis of the underlying recommendation algorithm.

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Cited By

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Jain KJindal R(2023)Sampling and noise filtering methods for recommender systems: A literature reviewEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106129122(106129)Online publication date: Jun-2023
https://doi.org/10.1016/j.engappai.2023.106129
Yang PAlvarado Rodriguez OGutierrez FVerbert K(2022)Touching the Explanations: Explaining Movie Recommendation Scores in Mobile Augmented Reality2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)10.1109/AIVR56993.2022.00032(157-162)Online publication date: Dec-2022
https://doi.org/10.1109/AIVR56993.2022.00032
Thakker UPatel RShah M(2021)A comprehensive analysis on movie recommendation system employing collaborative filteringMultimedia Tools and Applications10.1007/s11042-021-10965-280:19(28647-28672)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s11042-021-10965-2
Show More Cited By

Index Terms

Movie SCoRe: Personalized Movie Recommendation on Mobile Devices
1. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools
    2. Mobile information processing systems

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Abstract
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Highlights
- To deploy a matrix factorization model for the predictions and use them in the model.
- To generate handcrafted features of the top five similar users and items.
- To combine all features to make a hybrid collaborative filtering model.

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Information & Contributors

Information

Published In

PCI '17: Proceedings of the 21st Pan-Hellenic Conference on Informatics

September 2017

322 pages

ISBN:9781450353557

DOI:10.1145/3139367

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]

In-Cooperation

Greek Com Soc: Greek Computer Society
University of Thessaly: University of Thessaly, Volos, Greece

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 September 2017

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PCI 2017

PCI 2017: 21st PAN-HELLENIC CONFERENCE ON INFORMATICS

September 28 - 30, 2017

Larissa, Greece

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Overall Acceptance Rate 190 of 390 submissions, 49%

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Cited By

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Jain KJindal R(2023)Sampling and noise filtering methods for recommender systems: A literature reviewEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106129122(106129)Online publication date: Jun-2023
https://doi.org/10.1016/j.engappai.2023.106129
Yang PAlvarado Rodriguez OGutierrez FVerbert K(2022)Touching the Explanations: Explaining Movie Recommendation Scores in Mobile Augmented Reality2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)10.1109/AIVR56993.2022.00032(157-162)Online publication date: Dec-2022
https://doi.org/10.1109/AIVR56993.2022.00032
Thakker UPatel RShah M(2021)A comprehensive analysis on movie recommendation system employing collaborative filteringMultimedia Tools and Applications10.1007/s11042-021-10965-280:19(28647-28672)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s11042-021-10965-2
Panagiotakis CPapadakis HFragopoulou P(2018)Detection of Hurriedly Created Abnormal Profiles in Recommender Systems2018 International Conference on Intelligent Systems (IS)10.1109/IS.2018.8710589(499-506)Online publication date: 25-Sep-2018
https://dl.acm.org/doi/10.1109/IS.2018.8710589
Papadakis HFragopoulou PMichalakis NPanagiotakis C(2018)A Mobile Application for Personalized Movie Recommendations with Dynamic Updates2018 International Conference on Intelligent Systems (IS)10.1109/IS.2018.8710568(507-514)Online publication date: 25-Sep-2018
https://dl.acm.org/doi/10.1109/IS.2018.8710568

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Index Terms

Recommendations

Improving Accuracy of Recommender System by Item Clustering

The role of user mood in movie recommendations

Hybrid collaborative filtering using matrix factorization and XGBoost for movie recommendation