Building your own Recommender System - Part 3/4
Let's dive into building a content-based recommender system and learn how to leverage item attributes to craft personalized recommendations tailored to individual preferences.
👋 Hey! This is Manisha Arora from PrepVector. Welcome to the Tech Growth Series, a newsletter that aims to bridge the gap between academic knowledge and practical aspects of data science. My goal is to simplify complicated data concepts, share my perspectives on the latest trends, and share my learnings from building and leading data teams.
This is Part 3 of our ongoing "Building Your Own Recommender Systems" series! In the first two blogs, we explored the fundamentals of recommender systems—introducing the concept, understanding their high-level architecture, and discussing the different types such as content-based, collaborative filtering, and hybrid models. In Part 2, we delved into evaluating these systems, focusing on metrics that assess their effectiveness.
Now, we turn our attention to building a content-based recommender system, a foundational approach that uses item attributes to make personalized recommendations.
Getting Started
To demonstrate a content-based recommender system, we’ll implement a simple example using Python and Pandas, leveraging a movie dataset. Although libraries like scikit-surprise don't natively support content-based methods, they do offer flexible base classes for creating custom algorithms. For simplicity, however, we’ll begin with a manual implementation.
Step 1: Prepare the Data
We start by loading a movie dataset into a Pandas DataFrame. This dataset includes attributes such as movie titles, genres, and release years. To prepare the data for recommendation, we’ll create a combined key column with genres and year information.
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Step 2: Transform Text Data into Numeric Form
To calculate pairwise similarity between movies, we need to convert textual data into numerical form using text vectorization. Let’s discuss common vectorization methods before selecting one for our use case.
Types of Text Processing Vectorizers
Count Vectorizer
Converts text into a matrix of token counts.
Simple and interpretable, suitable for basic NLP tasks.
TF-IDF Vectorizer
Balances term frequency and corpus-wide importance.
Downweights common terms while emphasizing rare ones.
Word Embedding-Based Techniques
Word2Vec, GloVe, FastText: Capture semantic relationships between words.
Useful for advanced NLP tasks like text generation or translation.
Choosing the Right Vectorizer
For this example, we’ll use a simple Count Vectorizer, which works well for smaller datasets and straightforward tasks like this one.
Step 3: Implementing the Recommender
Once vectorized, we calculate the pairwise similarity matrix using cosine similarity. This matrix allows us to find movies that are most similar to a given title. Here’s the implementation:
Step 4: Generate Recommendations
Let’s test the system by recommending movies similar to "Avatar" (2009).
The recommendations include movies with similar genres, such as Action, Adventure, and Sci-Fi, and release years close to 2009, demonstrating the effectiveness of this simple content-based approach.
Closing Thoughts
This example highlights how content-based recommender systems use item attributes to generate relevant recommendations. By leveraging techniques like vectorization and similarity metrics, even basic implementations can offer valuable insights into user preferences.
Next up in this series, we’ll explore collaborative filtering techniques, which go beyond item attributes to incorporate user behavior and interactions. Stay tuned!
If you're following along with the series, let us know what you think about content-based systems or what specific topics you'd like us to cover in future posts. Let's keep building!
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