https://github.com/iamabhaytiwari343/movie-recommender
A content based movie recommender that recommend movies based on tags
https://github.com/iamabhaytiwari343/movie-recommender
bag-of-words machine-learning python recommendation similarity-measures
Last synced: 7 months ago
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A content based movie recommender that recommend movies based on tags
- Host: GitHub
- URL: https://github.com/iamabhaytiwari343/movie-recommender
- Owner: iamabhaytiwari343
- Created: 2023-04-02T13:44:14.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-06-08T17:28:20.000Z (over 1 year ago)
- Last Synced: 2025-01-22T06:19:48.528Z (9 months ago)
- Topics: bag-of-words, machine-learning, python, recommendation, similarity-measures
- Language: Jupyter Notebook
- Homepage:
- Size: 9.84 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# movie-recommender
A content based movie recommender that recommend movies based on tags
What is bag-of-Words algorithm ?
Bag of words is a Natural Language Processing technique of text modelling.
Bag of words is a Natural Language Processing technique of text modelling. In technical terms, we can say that it is a method of feature extraction with text data. This approach is a simple and flexible way of extracting features from documents.
A bag of words is a representation of text that describes the occurrence of words within a document. We just keep track of word counts and disregard the grammatical details and the word order. It is called a “bag” of words because any information about the order or structure of words in the document is discarded.
Why it is used here ?
we are using this method to convert the tags for a movie into vectors whoose similiarity-score can be used for movie recommendation.
ast.literal_eval() is a function provided by the ast (Abstract Syntax Trees) module in Python. This function is used to safely evaluate a string containing a Python literal or container display, like a dictionary, list, tuple, set, or basic data types like strings, numbers, booleans, and None. It's commonly used to convert a string representation of a literal into the corresponding Python object.The main advantage of ast.literal_eval() over other evaluation methods like eval() is that it is safer. It only evaluates literals and not arbitrary code, which helps prevent code injection and security vulnerabilities.