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The ultimate recommendation system: proposed Pranik System

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Abstract

In today's fast-paced world, recommendation systems have become indispensable tools, aiding users in making personalized decisions amidst an overwhelming array of choices. These systems leverage user data and preferences to generate tailor-made recommendations based on individual tastes and behaviors. This research paper introduces the development and implementation of Pranik Movies, an ultimate recommendation system for personalized movie suggestions. The system incorporates collaborative and content-based filtering techniques, utilizing machine learning algorithms to analyze user behaviors, ratings, and viewing histories. A comprehensive overview of the research framework is provided, encompassing system architecture, data pre-processing, feature engineering techniques, and model selection and design. Text processing methods such as stemming, bag-of-words (BoW), and TF-IDF (Term Frequency-Inverse Document Frequency) are employed for processing and analyzing textual movie data. The accuracy of recommendations is enhanced through the assessment of film similarities, utilizing algorithms like cosine similarity and Euclidean distance. The paper concludes by outlining future directions for advanced machine learning techniques, social media integration, expanded content support, and the refinement of the evaluation framework. Pranik Movies signifies a significant advancement in recommendation systems, enabling personalized and precise movie recommendations within a vast and diverse cinematic landscape.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Authors and Affiliations

  1. KIET Group of Institutions, Delhi NCR Ghaziabad, India

    Vipin Kumar, Amit Kumar Gupta, Nikhil Kumar & Rajeev Kumar

  2. Meerut Institute of Engineering & Technology, Meerut, India

    Ruchi Rani Garg

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  1. Vipin Kumar
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  2. Amit Kumar Gupta
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  3. Ruchi Rani Garg
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  4. Nikhil Kumar
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  5. Rajeev Kumar
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Correspondence to Rajeev Kumar.

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Kumar, V., Gupta, A.K., Garg, R.R. et al. The ultimate recommendation system: proposed Pranik System. Multimed Tools Appl 83, 43177–43198 (2024). https://doi.org/10.1007/s11042-023-17370-x

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