 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: March 2026
Volume 11 | Issue 3
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21686
Article Published : 8549
Total Authors : 22487
Total Reviewer : 811
Total Countries : 159
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | A Hybrid Deep Learning and Machine Learning Approach for Sentiment-Enhanced Movie Recommendations |
| Authors Name: | D Deepthiswarupa , G Vignesh , K Lasya Chowdary , Tejas M , Rajashree D Ingale |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_203485
|
| Published Paper Id: | IJRTI2505055 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | |
| Abstract: | In recent years, the integration of sentiment analysis into recommendation systems has gained considerable attention, enhancing the relevance and accuracy of suggestions provided to users. This paper presents a comprehensive hybrid approach combining machine learning (ML) and deep learning (DL) techniques to improve movie recommendation systems. By leveraging sentiment analysis of user reviews and blending collaborative filtering methods, the proposed system achieves superior recommendation precision and user satisfaction. We conduct extensive experiments using multiple models, including LSTM, CNN, SVM, and transformer-based architectures like BERT, across diverse datasets such as movie reviews, product feedback, and social media posts. The results demonstrate that hybrid models consistently outperform single-method baselines, providing a robust foundation for next-generation recommender systems. |
| Keywords: | Smart Surveillance, Internet of Things, Artificial Intelligence, Automation, Object Detection, Raspberry Pi, Real-time Monitoring |
| Cite Article: | "A Hybrid Deep Learning and Machine Learning Approach for Sentiment-Enhanced Movie Recommendations", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a530-a533, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505055.pdf |
| Downloads: | 000450 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2505055
Registration ID:203485
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: a530-a533 Country: Bengaluru, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505055 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505055 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||