The Journey of Predicting User Movie Preferences
Imagine a world where your online activities, from shopping to reading the news, could help predict your movie preferences. This project embarks on that very journey—using user data from e-commerce, news, education, and music categories to predict which movie genres users are most likely to enjoy.
The Problem We Set Out to Solve
We began with a fascinating challenge: could we predict which type of movies people prefer based on how they engage with different digital content? The data we collected covered a variety of categories:
- E-commerce: Users were categorized by their spending habits—expensive, normal, or cheap.
- News: They were grouped based on interests in sports, travel, or politics.
- Education: We noted their focus on business, psychology, or computer science.
- Music: We tracked their love for rock, pop, or hip-hop.
For each of the 10,000 users, we also gathered their preferences for movie genres, which included horror, action, and drama. Our goal was to train a model that could predict these movie preferences for new users based on their interests in the other categories.
The Modeling Process: Turning Data into Insight
The next step was feeding this treasure trove of data into a machine learning model, specifically a MultiOutputRegressor. This allowed us to predict multiple movie genres for users who hadn’t disclosed their movie preferences yet. Using Python’s powerful libraries like "scikit-learn" and "pandas," we were able to develop a robust prediction system without needing to build everything from scratch.
The Tools Behind the Magic: MultiOutputRegressor and Other Models
To achieve our goal of predicting multiple movie genres based on user behavior, we used a special model called MultiOutputRegressor. But what is it, and how does it work?
MultiOutputRegressor: Predicting More Than One Thing at Once
In traditional machine learning models, you typically predict just one outcome—like whether someone prefers action or horror movies. But what if you need to predict several things at once, like whether someone prefers action, horror, and drama, all based on their behavior in other areas?
That’s where MultiOutputRegressor comes in. It allows a single model to predict multiple outputs simultaneously. Instead of training one model for each movie genre, MultiOutputRegressor wraps around another model (Random Forest) and predicts all the genres together. It’s like having a multitasking expert who can handle multiple jobs at once.
Example:Imagine you’re trying to predict someone’s preference for three types of movies: horror, action, and drama. Instead of training three separate models (one for each genre), you can use MultiOutputRegressor, which builds one model that makes predictions for all three at the same time. This saves time and allows the model to see connections between the genres.
Random Forest: A Forest of Decision Trees
While Decision Trees are powerful, they can sometimes make mistakes by focusing too much on the specifics of one dataset. This is where Random Forest comes in. Instead of using just one Decision Tree, Random Forest builds many of them (hence the "forest") and combines their predictions. By averaging the results of all these trees, it makes a stronger and more accurate prediction.
Example:Imagine each Decision Tree in the Random Forest is like a movie critic. One critic might say, “Based on this user’s love of pop music, I think they’ll like drama.” Another critic might say, “Based on their preference for travel news, they might like horror.” By taking the opinions of many critics (trees), Random Forest averages their answers and gives a more balanced prediction.
Decision Tree: Making Decisions Like a Flowchart
A Decision Tree is one of the simplest machine learning models. It works like a series of yes/no questions that split the data into smaller and smaller groups, eventually leading to a prediction. You can think of it as a flowchart where each step asks a question, like “Does this user prefer expensive products?” or “Is this user interested in travel news?”
Example:Let’s say we have a new user, and we’re trying to figure out their movie preference. The Decision Tree might start with a question like, “Does the user prefer rock music?” If the answer is yes, the tree moves in one direction (maybe towards action movies). If the answer is no, it moves in another direction (maybe towards drama).
The tree keeps asking questions until it reaches a final prediction for the user’s movie genre. The simplicity of the Decision Tree makes it easy to understand, but it can sometimes be too basic, which is why we use more advanced models like Random Forest.
But there were challenges along the way.
Challenges and Creative Solutions
Our first challenge was the complexity of data collection. Initially, we included even more categories and subcategories, believing that more data would yield better results. However, to stay focused and simplify the process, we refined our categories and subcategories to a manageable yet meaningful set.
Then came visualization. With 12 input dimensions—representing user focus in four categories, each with three subcategories—it became impossible to visualize the relationships in a traditional 2D or 3D space.
What We Learned and What's Next
Our model worked well in predicting user preferences for movie genres, but we believe there’s potential for much more. In the future, we aim to expand this model to predict preferences in other domains beyond movies. We also see room for introducing more subcategories to capture a wider spectrum of user interests.
The possibilities don’t end there. Imagine a browser extension that gathers user data automatically and refines predictions in real time. Or consider diving even deeper into granular data—like the specific types of products users prefer in e-commerce, not just price ranges. These additional layers could bring an even sharper edge to our predictions.
A Real-World Example
Let’s bring this to life with a concrete example. Say we have a new user who has the following profile:
- E-commerce: Prefers normal-priced products.
- News: Loves travel-related news.
- Education: Focuses on business studies.
- Music: Enjoys pop music.
Our model would then search for the most similar users from the training data. Perhaps it finds someone who also prefers normal-priced items, is passionate about travel news, and studies business. Although this person prefers rock music, their overall profile is a close match. Based on that, our model predicts that the new user might enjoy horror or drama movies—genres that the similar user favored.
This isn’t just a prediction; it’s a glimpse into how patterns in everyday digital behavior can reveal deeper personal tastes.
Conclusion
This project demonstrates how diverse online habits can be linked in unexpected ways to personal entertainment preferences. And while the focus now is on movies, the broader applications of this work are limitless. We look forward to continuing this journey, expanding the scope, and exploring new horizons in user behavior prediction.