Unlocking the Power of Personalized TV Show Recommendations with Netflix's DCR
In the era of streaming, personalized content recommendations have become a crucial aspect of boosting user engagement and satisfaction. Netflix, a pioneer in this space, has developed a sophisticated recommendation system, known as the Dual-Criteria Recommender (DCR). At its core, the DCR uses a collaborative filtering approach to identify the most relevant TV shows for each user. In this post, we'll delve into the technical details of building a personalized TV show recommendation system using the Surprise library in Python.
Loading Ratings Data
To begin, we need to load our ratings data from a CSV file, assuming it's stored in a file named 'ratings.csv'. We use the Dataset.load_from_csv() function from the Surprise library to accomplish this.
python
from surprise import KNNWithMeans
from surprise import Dataset
# Load ratings from a file (assuming 'ratings.csv' exists)
data = Dataset.l...
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