Enhanced Movie Recommendation Framework Using LSTM and Meta Path Analysis with Hybrid Feature Fusion

DOI

10.2991/978-94-6463-718-2_126How to use a DOI?

Keywords

movie recommendation; LSTM; meta-path analysis; hybrid feature fusion; heterogeneous networks; user behavior modeling; scalability

Abstract

Based on this rapid increase of online streaming platforms, advanced recommendation systems are required to assist users in efficiently receiving personalized movie recommendations. We research and propose an Enhanced Movie Recommendation Framework based on Long Short-Term Memory (LSTM) networks combined with meta-path analysis and multi-feature hybrid fusion in order to utilize diversified user and content characteristics. The presented framework overcomes some of the pitfalls of conventional collaborative filtering and content-based systems by combining temporal user behavior modeling with LSTM and enhancing feature representations with meta-path-guided heterogeneous network embeddings. Multi-modal data fusion is designed to fuse user preference, context, and movie meta data together to improve the recommendation quality. We present experimental results to show the framework's advantages in the three dimensions: managing sparse data, enhancing scalability and providing intelligible reasons for recommendation decisions. This work represents an important step towards building practical recommendation systems that are robust, optimizable, and adaptable to dynamic environments.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Venkatesh Guru
AU  - J. Nirmala Gandhi
AU  - K. Venkatesan
AU  - P. Abinesh
AU  - A. Ajay Karthick
AU  - S. Deepak
PY  - 2025
DA  - 2025/05/23
TI  - Enhanced Movie Recommendation Framework Using LSTM and Meta Path Analysis with Hybrid Feature Fusion
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1512
EP  - 1523
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_126
DO  - 10.2991/978-94-6463-718-2_126
ID  - Guru2025
ER  -