International Journal For Multidisciplinary Research

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A Long Tail Item Recommendations for MovieLens

Author(s) Shruti Soma Gawas, Dr. Harshali Patil, Mustafa Aliasgar Kagdi, Dr. Jyotshna Dongardive
Country INDIA
Abstract Amidst the framework of the Movie-lens dataset, this research paper digs into the essential topic of long-tail item suggestions. Long tail items, which are frequently neglected in traditional recommendation systems, constitute a substantial reservoir of specialty material. We evaluate and analyze existing methodology and breakthroughs in this subject, including collaborative filtering and matrix factorization, as well as hybrid and deep learning-based approaches. Our assessment highlights the ongoing issue of improving recommendation accuracy and diversity, especially for less popular films. In this paper, we investigate the crucial importance of user-item interaction patterns and auxiliary data sources in tackling the long tail problem. We present a complete analysis of the state-of-the-art in long tail item suggestions for movie lenses by exploring the strengths and limits of various strategies. This study gives insightful information for long-tail recommendations looking to create more inclusive, user-centric, and interesting movie recommendation platforms. It also sheds light on the changing landscape of recommendation systems.
Keywords item recommendations, collaborative filtering, user-item interaction patterns, recommendation system, niche items.
Field Computer > Data / Information
Published In Volume 5, Issue 5, September-October 2023
Published On 2023-10-05
Cite This A Long Tail Item Recommendations for MovieLens - Shruti Soma Gawas, Dr. Harshali Patil, Mustafa Aliasgar Kagdi, Dr. Jyotshna Dongardive - IJFMR Volume 5, Issue 5, September-October 2023. DOI 10.36948/ijfmr.2023.v05i05.7096
DOI https://doi.org/10.36948/ijfmr.2023.v05i05.7096
Short DOI https://doi.org/gst3vh
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