A music recommender system using YouTube video descriptions of the songs. It involves using Natural Language Processing (NLP) techniques to analyze the text descriptions and recommend music based on the content.
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Data Collection:
- [Dataset Link:] (https://www.kaggle.com/datasets/notshrirang/spotify-million-song-dataset)
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Text Preprocessing:
- Clean and preprocess the text by removing special characters, punctuation, and converting all letters to lowercase.
- Tokenize the descriptions into individual words or phrases.
- Remove stopwords (common words like "and," "the," "is," etc.) that don't provide much context.
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Feature Extraction:
- Convert the tokenized descriptions into numerical representations that can be used by machine learning models. We can use techniques like TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings (Word2Vec, GloVe) for this purpose.
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Building a Recommender Model:
- Choose a recommendation algorithm. Collaborative Filtering and Content-Based Filtering are two common approaches.
Content-Based Filtering:
- In this case, content-based filtering might be more suitable since we're focusing on analyzing the video descriptions. This approach recommends items similar to those the user has shown interest in.
- Calculate similarity scores between videos based on their preprocessed descriptions and feature representations.
- Recommend videos that have similar descriptions to the ones the user has liked or interacted with in the past.
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User Interaction and Recommendations:
- Allow users to input their preferences, e.g., by providing a sample video URL or keywords related to their interests.
- Use the selected video's description for recommendation.
- Rank the videos based on similarity scores and present the top recommendations to the user.
