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![Example:
• Consider two documents: "The cat in the hat" and "The quick brown
fox."
• Vocabulary: ["The", "cat", "in", "hat", "quick", "brown", "fox"] (unique
words across both documents).
• Document Vectors:
⚬ "The cat in the hat": [1, 1, 1, 1, 0, 0, 0]
⚬ "The quick brown fox": [1, 0, 0, 0, 1, 1, 1]](https://image.slidesharecdn.com/mlppt-240205092635-c66db5ce/75/Machine_learning_presentation_on_movie_recomendation_system-pptx-23-2048.jpg)
Machine_learning_presentation_on_movie_recomendation_system.pptx
- 1. Movies Recommendation System Using ML Submitted By: SubmittedTo: Kruteeka Samal (2021UGELE16) Nagraj Sir Akash Vishwakarma (2021UGECE02)
- 2. Problem Statement Introduction toRecommendation System Project Flow Data Processing Text Vectorization UI building and Deployment Accuracy and Confusion Matrix Results Conclusion 01 02 03 04 05 06 07 08 09 Table of contents
- 3. Problem Statement In theera of digital entertainment, users often face the challenge of selecting movies from a vast library of options. To enhance the user experience and aid in decision-making, a movie recommendation system is desired. The system aims to recommend movies based on user preferences, leveraging collaborative filtering techniques.
- 4. What is aRecommendation System?
- 5. Recommendation System A recommendationsystem is a technology or algorithmic approach designed to suggest items or content to users based on their preferences, behaviors, or characteristics. The primary goal of a recommendation system is to predict what items or content a user may be interested in and present those recommendations in a personalized and relevant manner. These systems are widely used in various industries, including e-commerce, streaming services, social media, and more.
- 6. Content-Based Collabrative-Filtering Hybrid •Hybrid recommendation systems combine multiple approaches to provide more accurate and diverse recommendations. • For example, a system might integrate collaborative filtering and content-based filtering to leverage the strengths of both methods. Types • This method recommends items based on the characteristics or features of the items themselves and the user's preferences. • It involves analyzing the content of items and recommending items with similar attributes to those the user has shown interest in. • Collaborative filtering is a popular technique used in recommendation systems to provide personalized suggestions to users based on the preferences and behaviors of similar users. • There are two main types of collaborative filtering: user- based collaborative filtering and item-based collaborative filtering.
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- 8. 01 02 03 0405 Project Flow Data Collection Data Processing UI Buildin g Deployment Text Vectorization
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- 10. Data Processing Data processingin machine learning is about preparing raw data for model training. This includes cleaning data, transforming it for compatibility, and splitting it into training, validation, and testing sets. Effective data processing is crucial for models to perform well and generalize to new data.
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- 20. Text Vectorization Text vectorization isthe process of converting textual data into numerical vectors, which can be used as input for machine learning algorithms In natural language processing (NLP) and text analysis, representing text as numerical vectors is essential because most machine learning models operate on numerical data. Some common techniques for text vectorization: • Bag-of-Words (BoW) • Term Frequency-Inverse Document Frequency (TF-IDF) • N-grams • Word Embeddings • and many more...
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- 22. Tokenization Bag-of-Words (BoW) • Representsa document as an unordered set of words, ignoring grammar and word order. • It converts a document or a piece of text into a numerical vector, ignoring the order and structure of words while focusing on their occurrence in the document. • The first step is to break down the text into individual words or tokens. This process is called tokenization. Vocabulary Construction Word Counting • Create a vocabulary or a unique set of words present in the entire corpus (collection of documents). • Each word is assigned a unique index in the vocabulary. • For each word in the document, increment the corresponding index in the vector by one. • The resulting vector represents the count of each word in the document. Document Representation Sparse Representation • For each document in the corpus, create a vector with the length equal to the size of the vocabulary. • Initialize all values in the vector to zero. • Since most documents use only a small subset of the entire vocabulary, the resulting vectors are typically sparse (contain mostly zeros).
- 23. Example: • Consider twodocuments: "The cat in the hat" and "The quick brown fox." • Vocabulary: ["The", "cat", "in", "hat", "quick", "brown", "fox"] (unique words across both documents). • Document Vectors: ⚬ "The cat in the hat": [1, 1, 1, 1, 0, 0, 0] ⚬ "The quick brown fox": [1, 0, 0, 0, 1, 1, 1]
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