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

^*Bu çalışma için yazışmadan sorumlu yazar

Elektrik-Elektronik Fakültesi

Astana IT University
Izmir Bakircay University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations
Editörler	Ali Othman Albaji
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	332-345
Sayfa sayısı	14
ISBN (Basılı)	9783032002310
DOI'lar	https://doi.org/10.1007/978-3-032-00232-7_21
Yayın durumu	Yayınlandı - 2026
Etkinlik	International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025 - Tripoli, Libya Süre: 9 Tem 2025 → 10 Tem 2025

Yayın serisi

Adı	Studies in Computational Intelligence
Hacim	1229 SCI
ISSN (Basılı)	1860-949X
ISSN (Elektronik)	1860-9503

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025
Ülke/Bölge	Libya
Şehir	Tripoli
Periyot	9/07/25 → 10/07/25

Bibliyografik not

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

Belgeye Erişim

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

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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; Hac. 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. editör / 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\}",

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

address = "Germany",

}

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, hac. 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; Hac. 1229 SCI).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

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, editör, 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

Özet

Yayın serisi

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Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap