A Machine Learning Approach to Personalized Movie Recommendations Using Content-based Filtering, Web Scraping and NLP Based Sentiment Analysis
Keywords:
Content-based filtering, Cosine similarity, API key, Web scraping, Sentiment analysisAbstract
This research work presents an improved approach for movie recommendation using content-based filtering and cosine similarity. The proposed system considers the movies' content information and genre during item similarity calculations to recommend a top 10 list of similar movies based on user preferences. Data for the movies, including title, genre, runtime, rating, and cast, is obtained from the TMDB website using an API key, while reviews are web-scraped from the IMDB website and subjected to sentiment analysis using NLP to predict whether the review is positive or negative. The recommendation system is designed to minimize transaction costs and improve the quality and decision-making process for users. This statement highlights the significance of recommendation systems in today's world, emphasizing their widespread utilization by renowned applications.
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