With enriched data from NLP processing, such as keywords, topics, sentiment, and summarization, content-based filtering can be more nuanced. You can match movies based on the similarity of their plot descriptions, themes, and other extracted features, providing recommendations that are closely aligned with the user's interests.
This method remains effective and can be even more powerful when combined with NLP-enhanced content data. You can use user ratings or viewing patterns in conjunction with plot-based features to find movies that similar users enjoyed or that are similar in content to movies the user likes.
Input: Raw IMDb Database Output:
IMDb Database ---> Filtered Data
- Movies from 1980 to 2022