Enhancing Customer Engagement with AI-Driven Movie Recommenders: Integrating Neural Collaborative Filtering, Sentiment Analysis, and Conversational Agents

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]12/09/2025 at 10:04 AM[/if 2224] | [if 1553 equals=””] Volume : 12 [else] Volume : 12[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 45 54

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    Ikvinderpal Singh, Nidhi Aggarwal,

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  1. Assistant Professor, Assistant Professor, PG Department of Computer Science and Applications, Trai Shatabdi Guru Gobind Singh Khalsa College, Amritsar, PG Department of Commerce and Business Administration, BBK DAV College for Women, Amritsar, Punjab, Punjab, India, India

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Abstract

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nIn today’s competitive digital landscape, user engagement is a critical factor for the success of entertainment platforms, especially those offering movie recommendations. This study introduces a comprehensive AI-driven framework designed to enhance customer interaction, satisfaction, and loyalty through the intelligent integration of multiple deep learning models. The system combines three core components: Neural Collaborative Filtering (NCF) for generating personalized movie recommendations based on user behavior and preferences, Long Short-Term Memory (LSTM) networks for performing sentiment analysis on user-generated reviews, and Generative Pre-trained Transformer (GPT) models to serve as conversational agents for interactive user communication. By synergizing these technologies, the system is capable of not only delivering highly relevant recommendations but also interpreting user sentiments in real time and engaging users in natural, human-like dialogue. The framework is evaluated using the widely recognized MovieLens 100k dataset, which enables benchmarking the system’s effectiveness in a controlled environment. Results indicate substantial improvements in recommendation accuracy, sentiment classification performance, and user interaction quality. This multi-model approach provides a robust and scalable solution for enhancing the overall user experience on movie recommendation platforms. Ultimately, the proposed system represents a forward-thinking step toward more personalized, emotionally intelligent, and engaging digital entertainment services.nn

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Keywords: Customer engagement, neural collaborative filtering (NCF), long short-term memory (LSTM), generative pre-trained transformer (GPT), MovieLens dataset

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Artificial Intelligence Research & Advances ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Artificial Intelligence Research & Advances (joaira)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nIkvinderpal Singh, Nidhi Aggarwal. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Enhancing Customer Engagement with AI-Driven Movie Recommenders: Integrating Neural Collaborative Filtering, Sentiment Analysis, and Conversational Agents[/if 2584]. Journal of Artificial Intelligence Research & Advances. 07/08/2025; 12(03):45-54.

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How to cite this URL:
nIkvinderpal Singh, Nidhi Aggarwal. [if 2584 equals=”][226 striphtml=1][else]Enhancing Customer Engagement with AI-Driven Movie Recommenders: Integrating Neural Collaborative Filtering, Sentiment Analysis, and Conversational Agents[/if 2584]. Journal of Artificial Intelligence Research & Advances. 07/08/2025; 12(03):45-54. Available from: https://journals.stmjournals.com/joaira/article=07/08/2025/view=0

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  1. He X, Liao L, Zhang H, Nie L, Hu X, Chua TS. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 2017 Apr 3; 173–182.
  2. Zhang J, Geng YA, Li Q, Shi C. More than one: a cluster-prototype matching framework for zero-shot learning. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020 Oct 19; 1803–1812.
  3. Tan KL, Lee CP, Anbananthen KS, Lim KM. RoBERTa-LSTM: a hybrid model for sentiment analysis with transformer and recurrent neural network. IEEE Access. 2022 Feb 18; 10: 21517–25.
  4. Sharma A, Patel N, Gupta R. Enhancing Consumer Engagement through AI-powered Marketing Personalization: Leveraging Collaborative Filtering and Neural Networks. European Advanced AI Journal. 2021 Nov 5; 10(2).
  5. Rajassekharan D, Subramanian J, Lazar AJ, Pichuka Veera Venkata Satya S, Ray S, Ammasai V, Sengan S. Efficient Temporal Data Mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems. In International Conference on Intelligent Systems and Sustainable Computing. Singapore: Springer Nature Singapore; 2024 Dec 20; 199–210.
  6. Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I. Language models are unsupervised multitask learners. OpenAI blog. 2019 Feb 24; 1(8): 9.
  7. Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S. Language models are few-shot learners. Adv Neural Inf Process Syst. 2020; 33: 1877–901.
  8. Zou L, Xia L, Gu Y, Zhao X, Liu W, Huang JX, Yin D. Neural interactive collaborative filtering. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 2020 Jul 25; 749–758.
  9. Zhang Y, Zhao YF. A Web-based automated manufacturability analyzer and recommender for additive manufacturing (MAR-AM) via a hybrid machine learning model. Expert Syst Appl. 2022 Aug 1; 199: 117189.
  10. Dev J. The mathematics of bias for people prediction. XRDS: Crossroads, The ACM Magazine for Students. 2022 Jul 7; 28(4): 40–1.

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Volume 12
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 18/07/2025
Accepted 28/07/2025
Published 07/08/2025
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Publication Time 20 Days

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