Abstract
Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.
Original language | English |
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Title of host publication | Proceedings - 2016 IEEE Cyber Science and Technology Congress (CyberSciTech 2016), 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing (DASC 2016), 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing (PICom 2016), 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing (DataCom 2016) |
Subtitle of host publication | DASC-PICom-DataCom-CyberSciTech 2016 |
Editors | Randall Bilof |
Place of Publication | Piscataway, NJ (US) |
Publisher | IEEE |
Pages | 874-877 |
Number of pages | 4 |
ISBN (Print) | 978-1-5090-4065-0 |
DOIs | |
Publication status | Published - 11 Oct 2016 |
Event | 2016 IEEE Cyber Science and Technology Congress / 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing / 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing / 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing - Auckland, New Zealand Duration: 8 Aug 2016 → 10 Aug 2016 |
Conference
Conference | 2016 IEEE Cyber Science and Technology Congress / 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing / 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing / 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing |
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Abbreviated title | DASC-PICom-DataCom-CyberSciTech 2016 |
Country/Territory | New Zealand |
City | Auckland |
Period | 8/08/16 → 10/08/16 |
Bibliographical note
-© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Keywords
- collaboration
- computational modeling
- data models
- machine learning
- motion pictures
- predictive models
- training