In this project, we will develop a movie recommender system using cosine similarity and movie tags. The goal of the recommender system is to provide personalized movie recommendations to users based on their interests and preferences. We will use cosine similarity to measure the similarity between movie tags and recommend movies that are most similar to the ones a user has liked or rated highly.
For this project, we will use a dataset that contains information about movies and their corresponding tags. The dataset should include the following information:
- Movie IDs
- Movie Titles
- Movie Tags (a list of tags associated with each movie)
You can obtain such a dataset from online movie databases or use a dataset from MovieLens or TMDB.
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Data Preprocessing: Load the movie dataset and perform necessary preprocessing steps. This may include handling missing values, converting tags into a suitable format, and organizing the data for efficient retrieval.
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Tag Vectorization: Create a vector representation for each movie's tags. We will use techniques like TF-IDF (Term Frequency-Inverse Document Frequency) to convert tags into numerical vectors.
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Cosine Similarity Calculation: Implement the cosine similarity function to measure the similarity between two movie tag vectors. Cosine similarity calculates the cosine of the angle between two vectors and gives us a value between -1 and 1, where 1 indicates that the two vectors are exactly the same.
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User Input: Ask the user to input their favorite movie(s) or provide a list of movies they have liked or rated highly.
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Recommendation Generation: For each user-provided movie, retrieve its tag vector representation and calculate the cosine similarity between this vector and all the other movie vectors in the dataset. Sort the movies in descending order of similarity scores and select the top N movies as recommendations.
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Display Recommendations: Display the recommended movies to the user.
- Python will be the primary programming language for this project.
- Libraries such as NumPy, pandas, and scikit-learn may be used for data handling, vectorization, and cosine similarity calculations.
- You can use a simple command-line interface (CLI) to take user inputs and display movie recommendations.
To evaluate the effectiveness of our movie recommender system, we can use techniques like cross-validation and split the dataset into training and test sets. We can measure metrics like precision, recall, and F1-score to assess the performance of our system.
In this project, we have implemented a movie recommender system using cosine similarity and movie tags. The system takes user inputs of favorite movies and provides personalized movie recommendations based on the similarity of movie tags. With further optimizations and data improvements, the recommender system can be enhanced to provide even more accurate and relevant movie suggestions for users.