A Cross-Domain Recommender System for Movies, Books, and News: Leveraging Content-Based Filtering and Similarity Computation
Author(s): Mihir Patel, Bhaskar Das, N. G. Suhans Raj, Sri Gouri Reddy, Shivkumar Suwarnkar
Authors Affiliations:
- Student, Department of CSE(DS), Hyderabad Institute of Technology and Management, Hyderabad, India
- Associate Professsor, Department of CSE(DS), Hyderabad Institute of Technology and Management, Hyderabad,India
- Student, Department of CSE(DS), Hyderabad Institute of Technology and Management, Hyderabad, India
- Student, Department of CSE(DS), Hyderabad Institute of Technology and Management, Hyderabad, India
- Student, Department of CSE(DS), Hyderabad Institute of Technology and Management, Hyderabad, India
DOIs:10.2015/IJIRMF/202502031 | Paper ID: IJIRMF202502031
In an age of unprecedented digital content proliferation, users are often inundated with choices across various domains, such as movies, books, and news. The challenge lies in identifying and recommending relevant content that aligns with individual preferences while spanning multiple media types. To address this challenge, this research proposes a Cross-Domain Recommender System for Movies, Books, and News, which leverages advanced Natural Language Processing (NLP) techniques and content-based filtering to deliver cohesive and personalized recommendations. The system's objective is to break the siloed nature of traditional recommendation systems by enabling cross-domain content discovery. It begins by analyzing user preferences in one domain, such as movie genres or plot themes, and extrapolates this information to recommend similar or complementary content in other domains, including books and news. For instance, fans of dystopian films may receive recommendations for novels exploring similar themes or news articles on futuristic societal trends. This integration is achieved through the extraction and vectorization of features like genres, keywords, and summaries, enabling the computation of semantic similarities across domains. The methodology integrates vectorized representations, similarity metrics, and user profiling to establish a unified recommendation framework. Preliminary results highlight the system’s ability to enhance user engagement by fostering diverse content discovery. The findings demonstrate the feasibility of bridging domains through shared linguistic and thematic patterns, enriching the overall user experience. This study underscores the transformative potential of cross-domain recommender systems in personalizing content consumption. By employing NLP-driven techniques, the proposed system sets a foundation for intelligent, user-centric media navigation, paving the way for future innovations in recommendation technologies.
Mihir Patel, Bhaskar Das, N. G. Suhans Raj, Sri Gouri Reddy, Shivkumar Suwarnkar(2025); A Cross-Domain Recommender System for Movies, Books, and News: Leveraging Content-Based Filtering and Similarity Computation, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-11, Issue-2, Pp.199-208. Available on – https://www.ijirmf.com/
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