Movie Recommendation with Social Context: A Hybrid Deep Learning Approach

Amir Askarov; Fares A. Dael; Ibraheem Shayea; Kozhakhmet Zhaksylyk

doi:10.1007/978-3-032-00232-7_21

Movie Recommendation with Social Context: A Hybrid Deep Learning Approach

Amir Askarov
, Fares A. Dael^*
, Ibraheem Shayea
, Kozhakhmet Zhaksylyk

^*Corresponding author for this work

Faculty of Electrical and Electronics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper presents a hybrid deep learning framework for movie recommendation that leverages real-time Twitter data to address the limitations of static collaborative filtering. We propose a deep autoencoder architecture augmented with social context features (e.g., sentiment, trends) to model dynamic user preferences. Evaluated on the MovieTweetings dataset (200K ratings), our system reduces RMSE by 8.1% over SVD and 12.3% over k-NN, while outperforming recent GNN and transformer baselines. The study advances recommender systems by demonstrating the viability of social media integration, with implications for real-time personalization.

Original language	English
Title of host publication	Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations
Editors	Ali Othman Albaji
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	332-345
Number of pages	14
ISBN (Print)	9783032002310
DOIs	https://doi.org/10.1007/978-3-032-00232-7_21
Publication status	Published - 2026
Event	International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025 - Tripoli, Libya Duration: 9 Jul 2025 → 10 Jul 2025

Publication series

Name	Studies in Computational Intelligence
Volume	1229 SCI
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Conference

Conference	International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025
Country/Territory	Libya
City	Tripoli
Period	9/07/25 → 10/07/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

Deep Learning
Recommender Systems
Social Media Analysis
Twitter Data

Access to Document

10.1007/978-3-032-00232-7_21

Cite this

Askarov, A., Dael, F. A., Shayea, I., & Zhaksylyk, K. (2026). Movie Recommendation with Social Context: A Hybrid Deep Learning Approach. In A. O. Albaji (Ed.), Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations (pp. 332-345). (Studies in Computational Intelligence; Vol. 1229 SCI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-00232-7_21

Askarov, Amir ; Dael, Fares A. ; Shayea, Ibraheem et al. / Movie Recommendation with Social Context : A Hybrid Deep Learning Approach. Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations. editor / Ali Othman Albaji. Springer Science and Business Media Deutschland GmbH, 2026. pp. 332-345 (Studies in Computational Intelligence).

@inproceedings{012d5b9ef2754a50ba62c2db2d892a75,

title = "Movie Recommendation with Social Context: A Hybrid Deep Learning Approach",

abstract = "This paper presents a hybrid deep learning framework for movie recommendation that leverages real-time Twitter data to address the limitations of static collaborative filtering. We propose a deep autoencoder architecture augmented with social context features (e.g., sentiment, trends) to model dynamic user preferences. Evaluated on the MovieTweetings dataset (200K ratings), our system reduces RMSE by 8.1\% over SVD and 12.3\% over k-NN, while outperforming recent GNN and transformer baselines. The study advances recommender systems by demonstrating the viability of social media integration, with implications for real-time personalization.",

keywords = "Deep Learning, Recommender Systems, Social Media Analysis, Twitter Data",

author = "Amir Askarov and Dael, \{Fares A.\} and Ibraheem Shayea and Kozhakhmet Zhaksylyk",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025 ; Conference date: 09-07-2025 Through 10-07-2025",

year = "2026",

doi = "10.1007/978-3-032-00232-7\_21",

language = "English",

isbn = "9783032002310",

series = "Studies in Computational Intelligence",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "332--345",

editor = "Albaji, \{Ali Othman\}",

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}

Askarov, A, Dael, FA, Shayea, I & Zhaksylyk, K 2026, Movie Recommendation with Social Context: A Hybrid Deep Learning Approach. in AO Albaji (ed.), Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations. Studies in Computational Intelligence, vol. 1229 SCI, Springer Science and Business Media Deutschland GmbH, pp. 332-345, International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025, Tripoli, Libya, 9/07/25. https://doi.org/10.1007/978-3-032-00232-7_21

Movie Recommendation with Social Context: A Hybrid Deep Learning Approach. / Askarov, Amir; Dael, Fares A.; Shayea, Ibraheem et al.
Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations. ed. / Ali Othman Albaji. Springer Science and Business Media Deutschland GmbH, 2026. p. 332-345 (Studies in Computational Intelligence; Vol. 1229 SCI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Movie Recommendation with Social Context

T2 - International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025

AU - Askarov, Amir

AU - Dael, Fares A.

AU - Shayea, Ibraheem

AU - Zhaksylyk, Kozhakhmet

PY - 2026

Y1 - 2026

N2 - This paper presents a hybrid deep learning framework for movie recommendation that leverages real-time Twitter data to address the limitations of static collaborative filtering. We propose a deep autoencoder architecture augmented with social context features (e.g., sentiment, trends) to model dynamic user preferences. Evaluated on the MovieTweetings dataset (200K ratings), our system reduces RMSE by 8.1% over SVD and 12.3% over k-NN, while outperforming recent GNN and transformer baselines. The study advances recommender systems by demonstrating the viability of social media integration, with implications for real-time personalization.

AB - This paper presents a hybrid deep learning framework for movie recommendation that leverages real-time Twitter data to address the limitations of static collaborative filtering. We propose a deep autoencoder architecture augmented with social context features (e.g., sentiment, trends) to model dynamic user preferences. Evaluated on the MovieTweetings dataset (200K ratings), our system reduces RMSE by 8.1% over SVD and 12.3% over k-NN, while outperforming recent GNN and transformer baselines. The study advances recommender systems by demonstrating the viability of social media integration, with implications for real-time personalization.

KW - Deep Learning

KW - Recommender Systems

KW - Social Media Analysis

KW - Twitter Data

UR - https://www.scopus.com/pages/publications/105028297492

U2 - 10.1007/978-3-032-00232-7_21

DO - 10.1007/978-3-032-00232-7_21

M3 - Conference contribution

AN - SCOPUS:105028297492

SN - 9783032002310

T3 - Studies in Computational Intelligence

SP - 332

EP - 345

BT - Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations

A2 - Albaji, Ali Othman

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 9 July 2025 through 10 July 2025

ER -

Askarov A, Dael FA, Shayea I, Zhaksylyk K. Movie Recommendation with Social Context: A Hybrid Deep Learning Approach. In Albaji AO, editor, Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations. Springer Science and Business Media Deutschland GmbH. 2026. p. 332-345. (Studies in Computational Intelligence). doi: 10.1007/978-3-032-00232-7_21

Movie Recommendation with Social Context: A Hybrid Deep Learning Approach

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