Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Firefly Algorithm and Deep Neural Network Approach for Intrusion Detection Read chapter Read first chapter

2022 | OriginalPaper | Chapter

Movie Recommendation Based on Fully Connected Neural Network with Matrix Factorization

Authors : Vineet Shrivastava, Suresh Kumar

Published in: Applications of Artificial Intelligence and Machine Learning

Publisher: Springer Nature Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Recommendation System (RS) is a subclass of information filtering system which tracks “rating” given by users to in-use items. Movie RS act as a tool to provide services to users. With the massive increase of data on internet, users face challenge in searching information and sometimes it is time-consuming process. Hence there is a need to enhance RS in a way to resolve basic problems faced by the users during the search. In this research a hybrid technique is proposed to build a movie recommendation system. In this system, matrix factorization is used to know, low dimensional embeddings of movies and users. Thus, movie and user embeddings are combined to examine ratings on unseen movies. Experiments are done on the MovieLens dataset to examine the performance of the proposed movie RS. The hybrid technique in the paper achieves a better performance than other non-hybrid recommendation methods.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Predicting Deflagration and Detonation in Detonation Tube
next chapter PropFND: Propagation Based Fake News Detection
Literature
1.
Wei J, He J, Chen K, Zhou Y, Tang Z (2017) Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst Appl 69:29–39CrossRef
2.
Hwangbo H, Kim YS, Cha KJ (2018) Recommendation system development for fashion retail e-commerce. Electron Commer Res Appl 28:94–101CrossRef
3.
Su J (2016) Content based recommendation system. Google Patents
4.
Jiang L, Cheng Y, Yang L, Li J, Yan H, Wang X (2019) A trust-based collaborative filtering algorithm for E-commerce recommendation system. J Ambient Intell Humaniz Comput 10:3023–3034CrossRef
5.
Hsiao JH, Liu N, Li J (2019) E-commerce recommendation system and method. Google Patents
6.
Singh T, Nayyar A, Solanki A (2020) Multilingual opinion mining movie recommendation system using RNN. In: Proceedings of first international conference on computing, communications, and cyber-security (IC4S 2019), pp 589–605
7.
Cui Z, Xu X, Xue F, Cai X, Cao Y, Zhang W et al (2020) Personalized recommendation system based on collaborative filtering for IoT scenarios. IEEE Trans Services Comput 13:685–695CrossRef
8.
Wang M, Liu X, Jing L (2020) Deep learning based recommedation system: a review of recent works. In: 2020 IEEE 5th information technology and mechatronics engineering conference (ITOEC), pp 1245–1252
9.
Zhao L, Lu Z, Pan SJ, Yang Q (2016) Matrix factorization+ for movie recommendation. In: IJCAI, pp 3945–3951
10.
Singhal A, Sinha P, Pant R (2017) Use of deep learning in modern recommendation system: a summary of recent works. arXiv preprint arXiv:​1712.​07525
11.
Ibrahim M, Bajwa IS, Ul-Amin R, Kasi B (2019) A neural network-inspired approach for improved and true movie recommendations. Comput Intell Neurosci 2019:1–19CrossRef
12.
Aljunid MF, Manjaiah D (2019) Movie recommender system based on collaborative filtering using apache spark. In: Data management, analytics and innovation. Springer, Heidelberg, pp 283–295
13.
Reddy S, Nalluri S, Kunisetti S, Ashok S, Venkatesh B (2019) Content-based movie recommendation system using genre correlation. In: Smart intelligent computing and applications. Springer, Heidelberg, pp 391–397
14.
Virk HK, Singh EM, Singh A (2015) Analysis and design of hybrid online movie recommender system. Int J Innov Eng Technol (IJIET) 5:1–5
15.
Adeniyi DA, Wei Z, Yongquan Y (2016) Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Appl Comput Inf 12:90–108
16.
Subramaniyaswamy V, Logesh R, Chandrashekhar M, Challa A, Vijayakumar V (2017) A personalised movie recommendation system based on collaborative filtering. Int J High Perform Comput Netw 10:54–63CrossRef
17.
Rajarajeswari S, Naik S, Srikant S, Prakash MS, Uday P (2019) Movie recommendation system. In: Emerging research in computing, information, communication and applications. Springer, Heidelberg, pp 329–340
18.
Yi B, Shen X, Liu H, Zhang Z, Zhang W, Liu S et al (2019) Deep matrix factorization with implicit feedback embedding for recommendation system. IEEE Trans Ind Inf 15:4591–4601CrossRef
19.
Zhao J, Geng X, Zhou J, Sun Q, Xiao Y, Zhang Z et al (2019) Attribute mapping and autoencoder neural network based matrix factorization initialization for recommendation systems. Knowl-Based Syst 166:132–139CrossRef
20.
Tang D, Qin B, Liu T (2015) Deep learning for sentiment analysis: successful approaches and future challenges. Wiley Interdisc Rev Data Min Knowl Disc 5:292–303CrossRef
21.
Keras K (2019) Deep learning library for theano and tensorflow
Metadata
Title
Movie Recommendation Based on Fully Connected Neural Network with Matrix Factorization
Authors
Vineet Shrivastava
Suresh Kumar
Copyright Year
2022
Publisher
Springer Nature Singapore
Book
Applications of Artificial Intelligence and Machine Learning

Print ISBN: 978-981-19-4830-5

Electronic ISBN: 978-981-19-4831-2

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-981-19-4831-2_44

Premium Partner

    Image Credits