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Music recommendation system using million song dataset in machine learning with many approaches like popularity-based, content-based, collaborative-based.

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A music recommendation system is built with two solutions: similarity computation and Singular Value Decomposition (SVD).

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Recommend songs to user-based on Popularity based, Filtering, Content-Based Filtering and Collaborative based Filtering

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Facial expressions of a real person can be detected through webcam or any image and music is suggested according to their mood.

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基于KNN算法的个性化音乐推荐系统设计及实现

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In this project, I looked at how to recommend the top 10 songs to users based on the likelihood of listening to those songs. The core dataset is the Taste Profile Subset released by The Echo Nest as part of the Million Song Dataset. The two datasets are large: 1000000 rows and 5 columns in song_data.csv and 2000000 rows and 3 columns in count_data.csv. Data is highly sparse: only 1% of data have user-song interaction.

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Using deep learning to customize a playlist based on user's feedback on a subset of per-selected songs 🎹

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Music recommendation System using Spark, Hadooop

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Music recommendation system

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The final project for AAI 627: Data Acquisition, Modeling and Analysis: Big Data whilst pursuing a Master's in Applied Artificial Intelligence.

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Music recommendations

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To recommend user list of music based on 20 different characteristics or features of music.

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System recommender for music.

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This is music recommendation system who can recommend you a music based on your selected Artist and Genre . It can also recommend music by searching or selecting any specific song from database.

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