Build a Python-based podcast recommendation engine

Creating a Python-based podcast recommendation engine involves several key steps: data collection, preprocessing, feature extraction, similarity calculation, and generating recommendations. Below is a detailed implementation example of a simple content-based podcast recommendation system using podcast metadata (like title, description, categories, and tags).

Step 1: Data Preparation

You need podcast data that includes at least titles, descriptions, and categories or tags. For demonstration, let’s create a small sample dataset.

python
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import pandas as pd

podcasts = [
    {
        "id": 1,
        "title": "Tech Talk Daily",
        "description": "Daily updates on the latest technology trends and gadgets.",
        "categories": "Technology, Gadgets, News"
    },
    {
        "id": 2,
        "title": "History Uncovered",
        "description": "Exploring fascinating stories from world history.",
        "categories": "History, Education"
    },
    {
        "id": 3,
        "title": "Mindful Meditation",
        "description": "Guided meditation and mindfulness practices.",
        "categories": "Health, Wellness, Meditation"
    },
    {
        "id": 4,
        "title": "Science Weekly",
        "description": "Weekly discussions on recent scientific discoveries.",
        "categories": "Science, Technology, Education"
    },
    {
        "id": 5,
        "title": "Gourmet Kitchen",
        "description": "Delicious recipes and cooking tips for food lovers.",
        "categories": "Food, Cooking, Lifestyle"
    }
]

df = pd.DataFrame(podcasts)

Step 2: Text Preprocessing and Feature Extraction

Combine the podcast metadata (title, description, categories) into a single text feature and use TF-IDF Vectorizer to transform the text into vectors.

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from sklearn.feature_extraction.text import TfidfVectorizer

# Combine text fields
df['combined_features'] = df['title'] + " " + df['description'] + " " + df['categories']

# Initialize TF-IDF Vectorizer
tfidf = TfidfVectorizer(stop_words='english')

# Fit and transform the combined features
tfidf_matrix = tfidf.fit_transform(df['combined_features'])

Step 3: Compute Similarity Matrix

Use cosine similarity to compute the similarity between podcast vectors.

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from sklearn.metrics.pairwise import cosine_similarity

cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)

Step 4: Recommendation Function

Create a function to get recommendations based on a podcast title.

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def get_recommendations(title, cosine_sim=cosine_sim, df=df):
    # Get the index of the podcast that matches the title
    idx = df.index[df['title'].str.lower() == title.lower()]
    
    if len(idx) == 0:
        return "Podcast not found."
    
    idx = idx[0]
    
    # Get similarity scores for this podcast
    sim_scores = list(enumerate(cosine_sim[idx]))
    
    # Sort podcasts based on similarity scores, ignoring the first one (itself)
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)[1:]
    
    # Get the top 5 similar podcasts
    top_podcasts_indices = [i[0] for i in sim_scores[:5]]
    
    # Return titles of top similar podcasts
    return df.iloc[top_podcasts_indices][['title', 'description', 'categories']]

Step 5: Testing the Recommendation Engine

Example usage:

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recommended = get_recommendations("Tech Talk Daily")
print(recommended)

Additional Notes:

• For better recommendations, you could include user ratings or listening history and build a hybrid system combining collaborative filtering.

• You can expand the dataset with real podcast data from public APIs like Listen Notes or iTunes Search API.

• Text preprocessing can be enhanced by lemmatization or stemming.

• Use advanced embeddings like BERT for better semantic understanding if needed.

This simple content-based system recommends podcasts similar in topic and description to the queried podcast title, offering a solid foundation for more complex recommendation engines.