Building a content-based recommendation engine model using Adamic Adar Measure; A Netflix case study

Havolli, Abdullah; Maraj, Arianit; Fetahu, Lorik

doi:10.1109/meco55406.2022.9797139

Cited by 3 publications

(2 citation statements)

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“…The results show the connection of the edge and present all the relations between nodes. His future work will incorporate Artificial Intelligence and Machine Learning algorithms to find similarities between movie algorithms [8].…”

Section: Literature Reviewmentioning

confidence: 99%

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Revolutionizing Netflix Recommendations: The Power of Transfer Learning

Anuradha,

2023

Artificial Intelligence and Machine Learning Applications to Computational Sanskrit, Natural Language Processing and Cognition

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Major e-commerce companies grapple with managing vast data sets, posing a challenge to sense and fulfill user preferences. Despite existing Recommendation Systems (RS) for movies, songs, and products, misconceptions in recommendations persist. Navigating Netflix's extensive library poses selection challenges for users. This paper introduces an advanced recommender system utilizing text-based similarity and Word2Vec embedding. The model suggests movies based on diverse attributes like titles, genres, and descriptions. Employing techniques such as Transfer Learning, Similarity Matrix creation, and Content-Based Filtering with Word2Vec embedding, the system offers a top-10 list resembling the user's selected title. Validated through A/B testing, the user interface allows inputting preferred Netflix titles, delivering personalized recommendations based on content attributes, enhancing the streaming experience.

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Section: Literature Reviewmentioning

confidence: 99%

“…k is the rank at which we are evaluating the results. Once the result of DCG is obtained, NDCG, which is the Normalized DCG can be computed using the formula presented in (8).…”

Section: Discounted Cumulative Gain (Dcg)mentioning

confidence: 99%