TY - GEN
T1 - Movie Tag Prediction Using Multi-label Classification with BERT
AU - Cakar, Mahmut
AU - Aytekin, Tevfik
AU - Ozcan, Hunkar
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Recommendation systems are essential in optimizing user engagement on Over-the-Top (OTT) and Video-On-Demand (VOD) platforms. The conventional collaborative filtering approach, though effective, faces the cold-start challenge due to an undeveloped user base for new contents. To address this, many platforms turn to metadata-based recommendations; however, this method often struggles with content lacking rich metadata. This research introduces a novel solution that utilizes textual content—overviews, reviews, plots, and subtitles—to generate enhanced content descriptions. By integrating the BERT model, tailored for multi-label classification, and training it on the curated MovieLens Tag Genome 2021 dataset, we achieved dual outcomes: an improved similarity matrix using cosine similarity and a tag extraction system that aids in creating custom categories. This approach not only enhances content recommendation but also offers a feedback mechanism, promising a more enriched and personalised user experience.
AB - Recommendation systems are essential in optimizing user engagement on Over-the-Top (OTT) and Video-On-Demand (VOD) platforms. The conventional collaborative filtering approach, though effective, faces the cold-start challenge due to an undeveloped user base for new contents. To address this, many platforms turn to metadata-based recommendations; however, this method often struggles with content lacking rich metadata. This research introduces a novel solution that utilizes textual content—overviews, reviews, plots, and subtitles—to generate enhanced content descriptions. By integrating the BERT model, tailored for multi-label classification, and training it on the curated MovieLens Tag Genome 2021 dataset, we achieved dual outcomes: an improved similarity matrix using cosine similarity and a tag extraction system that aids in creating custom categories. This approach not only enhances content recommendation but also offers a feedback mechanism, promising a more enriched and personalised user experience.
KW - BERT
KW - Natural Language Processing
KW - Recommendation Systems
KW - Tag Prediction
UR - http://www.scopus.com/inward/record.url?scp=85208273101&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-71079-7_3
DO - 10.1007/978-3-031-71079-7_3
M3 - Conference contribution
AN - SCOPUS:85208273101
SN - 9783031710780
T3 - Communications in Computer and Information Science
SP - 31
EP - 40
BT - Computer and Communication Engineering - 4th International Conference, CCCE 2024, Revised Selected Papers
A2 - Neri, Filippo
A2 - Du, Ke-Lin
A2 - San-Blas, Angel-Antonio
A2 - Jiang, Zhiyu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Computer and Communication Engineering, CCCE 2024
Y2 - 24 May 2024 through 26 May 2024
ER -
