More Web Proxy on the site http://driver.im/

research-article

Towards a Personalized Movie Recommendation System: A Deep Learning Approach

Authors:

Mingjun ZhangAuthors Info & Claims

ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems

Article No.: 216, Pages 1 - 5

https://doi.org/10.1145/3469213.3470423

Published: 18 August 2021 Publication History

Abstract

In view of the limitations of existing personalized movie recommendation algorithms, in this article, we propose a personalized movie recommendation system based on deep neural networks. The system uses a deep neural network to process discrete features to fully dig out the various features between the user and the movie, and construct a user feature vector model and a movie feature vector model with demographic characteristics. Specifically, we introduce an attention mechanism into the model to learn the user's behavioral preferences and integrate user feature vectors to obtain the user's dynamic short-term interest features and static long-term historical interest features. This method enhances the recommendation performance of the model. We conduct performance exploration on the public dataset MovieLens, and the proposed recommendation model is superior to traditional recommendation algorithms in terms of accuracy and recall, and has higher recommendation performance.

References

[1]

Covington P, Adams J, Sargin E. Deep neural networks for youtube recommendations[C]//Proceedings of the 10th ACM conference on recommender systems. 2016: 191-198.

[2]

Elkahky A M, Song Y, He X. A multi-view deep learning approach for cross domain user modeling in recommendation systems[C]//Proceedings of the 24th international conference on world wide web. 2015: 278-288.

[3]

Wang H, Yeung D Y. Towards Bayesian deep learning: A framework and some existing methods[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(12): 3395-3408.

Digital Library

[4]

Song Y, Elkahky A M, He X. Multi-rate deep learning for temporal recommendation[C]//Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 2016: 909-912.

[5]

Xu C, Wu M. Learning Feature Interactions with Lorentzian Factorization Machine[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(04): 6470-6477.

[6]

Zhang S, Yao L, Sun A, Deep learning based recommender system: A survey and new perspectives[J]. ACM Computing Surveys (CSUR), 2019, 52(1): 1-38.

Digital Library

[7]

Lei C, Liu D, Li W, Comparative deep learning of hybrid representations for image recommendations[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 2545-2553.

[8]

Wu Y, Liu Y, Ahmed S H, Dominant data set selection algorithms for electricity consumption time-series data analysis based on affine transformation[J]. IEEE Internet of Things Journal, 2019, 7(5): 4347-4360.

[9]

Liu Y, James J Q, Kang J, Privacy-preserving traffic flow prediction: A federated learning approach[J]. IEEE Internet of Things Journal, 2020, 7(8): 7751-7763.

[10]

Wang X, Wang Y, Hsu D, Exploration in interactive personalized music recommendation: a reinforcement learning approach[J]. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2014, 11(1): 1-22.

Digital Library

[11]

Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[J]. IEEE transactions on knowledge and data engineering, 2005, 17(6): 734-749.

[12]

Kwon Y, Lee Y, Rhu M. Tensordimm: A practical near-memory processing architecture for embeddings and tensor operations in deep learning[C]//Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture. 2019: 740-753.

[13]

Miller B N, Konstan J A, Riedl J. Pocketlens: Toward a personal recommender system[J]. ACM Transactions on Information Systems (TOIS), 2004, 22(3): 437-476.

[14]

Shen X, Yi B, Liu H, Deep Variational Matrix Factorization with Knowledge Embedding for Recommendation System[J]. IEEE Transactions on Knowledge and Data Engineering, 2019.

[15]

Diao Q, Qiu M, Wu C Y, Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014: 193-202.

[16]

Zhang Y, Li Y, Wang R, PSAC: Proactive sequence-aware content caching via deep learning at the network edge[J]. IEEE Transactions on Network Science and Engineering, 2020, 7(4): 2145-2154.

Cited By

Li X(2024)Research on Recommendation Algorithm Based on Improved Collaborative Filtering2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA)10.1109/ICIPCA61593.2024.10709169(698-702)Online publication date: 28-Jun-2024
https://doi.org/10.1109/ICIPCA61593.2024.10709169
Mu YWu Y(2023)Multimodal Movie Recommendation System Using Deep LearningMathematics10.3390/math1104089511:4(895)Online publication date: 10-Feb-2023
https://doi.org/10.3390/math11040895
Mondal PChakder DRaj SSaha SOnoe NHong JLanperne MPark JCerny TShahriar H(2023)Graph Convolutional Neural Network for Multimodal Movie RecommendationProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing10.1145/3555776.3577853(1633-1640)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3555776.3577853
Show More Cited By

Recommendations

Item Based Collaborative Filtering Approach in Movie Recommendation System Using Different Similarity Measures
ICCTA '20: Proceedings of the 2020 6th International Conference on Computer and Technology Applications

Recommender systems use the concept of data analysis and knowledge discovery to essentially provide personalized recommendations on the web. A number of different algorithms has been proposed to effectively capture users interest and provide accurate ...
An algorithm for movie classification and recommendation using genre correlation

Collaborative filtering (CF), a technique used by recommendation systems, predicts and recommends items (information, products or services) that the user might like. Amazon.com's recommender system is one of the most famous examples of CF. ...
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 ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems

May 2021

2053 pages

ISBN:9781450390200

DOI:10.1145/3469213

Copyright © 2021 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: 18 August 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICAIIS 2021

ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems

May 28 - 30, 2021

Chongqing, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
208
Total Downloads

Downloads (Last 12 months)45
Downloads (Last 6 weeks)5

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li X(2024)Research on Recommendation Algorithm Based on Improved Collaborative Filtering2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA)10.1109/ICIPCA61593.2024.10709169(698-702)Online publication date: 28-Jun-2024
https://doi.org/10.1109/ICIPCA61593.2024.10709169
Mu YWu Y(2023)Multimodal Movie Recommendation System Using Deep LearningMathematics10.3390/math1104089511:4(895)Online publication date: 10-Feb-2023
https://doi.org/10.3390/math11040895
Mondal PChakder DRaj SSaha SOnoe NHong JLanperne MPark JCerny TShahriar H(2023)Graph Convolutional Neural Network for Multimodal Movie RecommendationProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing10.1145/3555776.3577853(1633-1640)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3555776.3577853
Jiang XFang X(2023)A novel high-utility association rule mining method and its application to movie recommendationMultimedia Tools and Applications10.1007/s11042-023-17063-583:14(41033-41049)Online publication date: 11-Oct-2023
https://doi.org/10.1007/s11042-023-17063-5
Chakder DMondal PRaj SSaha SGhosh AOnoe N(2022)Graph Network based Approaches for Multi-modal Movie Recommendation System2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53654.2022.9945488(409-414)Online publication date: 9-Oct-2022
https://doi.org/10.1109/SMC53654.2022.9945488
Pingali SMondal PChakder DSaha SGhosh A(2022)Towards Developing a Multi-Modal Video Recommendation System2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892382(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892382

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.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten