International Journal For Multidisciplinary Research

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Enhancing Book Recommendations: A Comprehensive Exploration of Cosine Similarity in Collaborative Filtering for Personalized Reading Experiences

Author(s) Arvind Nair, Anujkumar Baghel, Aryaman More, Tanush Shetty
Country India
Abstract Recommendation systems have become integral components across a myriad of services, spanning online shopping, music, and movie platforms. Pioneering companies such as Amazon have played a pivotal role in the evolution of collaborative filtering algorithms, which actively suggest items to users based on their preferences. In the realm of music, services like Pandora leverage an intricate understanding of up to 450 distinctive characteristics of songs, tailoring recommendations to align with users' unique musical tastes. Similarly, platforms like Spotify utilize the collective preferences of similar users to curate weekly song recommendations and personalized radio stations. The pervasive influence of recommendation systems extends to popular streaming services like Netflix, where these algorithms shape the content consumption experiences of millions by suggesting movies aligned with viewers' preferences. The impact of recommendation systems on the daily lives of consumers is substantial, influencing the materi-als they engage with regularly.
Keywords Recommendation System
Field Computer
Published In Volume 5, Issue 6, November-December 2023
Published On 2023-12-20
Cite This Enhancing Book Recommendations: A Comprehensive Exploration of Cosine Similarity in Collaborative Filtering for Personalized Reading Experiences - Arvind Nair, Anujkumar Baghel, Aryaman More, Tanush Shetty - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.10825
DOI https://doi.org/10.36948/ijfmr.2023.v05i06.10825
Short DOI https://doi.org/gs98sv
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