Recommender Systems

You recently bought a phone and now all you can see on your brand-new phone are ads for more such mobile phones. You’re probably wondering why. How does Instagram, Facebook, or even YouTube know that you were searching for a phone just a few days back? What sorcery is this?! Let me tell you how.

Well, it certainly isn’t sorcery but it’s even better! What if I told you could do something like this yourself?

Most apps that we use today depend on tools such as recommender systems to recommend relevant products to their customers. Recommender System is a sub-class of machine learning that uses large amounts of data collected from their users over time to analyze their users’ interests and habits to predict, narrow down, and help people look for what they need among the vast number of options available.

Amazon boasts about having over 350 million products and services. Now imagine you need to find just one product out of the millions of products available. If you had to sit and search through all of them one by one it would take years for you to find the particular product you were looking for. To make it easier for both the consumer and the provider recommender systems are used.

Types of recommender systems -

There are three broad categories under which recommender systems can be categorized:

1. Content-based filtering — This approach takes into account the features and attributes of the product to recommend similar items to the user. It analyzes the history of user interaction with the recommender system to further predict the preferences of the user.

2. Collaborative-filtering — This algorithm recommends taking into account the preferences of multiple users instead of just one. This model analyzes the user’s past interaction with a particular product along with interactions of other users who’ve interacted with the same product to predict the future interaction of the user. For example, if some users have watched similar movies or purchased similar products in the past there is a high probability that there’ll be a similarity of choices in the future.

3. Hybrid Systems — This approach is a combination of both and is used to build a more comprehensive recommender system. It combines the advantages of both algorithms to provide better suggestions.

Here’s a short roadmap to building your recommender system:

1. The first step is to define the problem. Why exactly do you need to use a recommendation system? Do you want it to suggest songs, movies, or a particular kind of fashion? This will help you to collect the data that is necessary to build the perfect recommender system.

2. Data collection and preprocessing. Assemble all the data you need for your project and make the necessary changes if required. Preprocessing may include removing null values, vectorizing text-based data, and so on. Pandas and NumPy are popular libraries used for preprocessing large amounts of data.

3. Exploratory data analysis — This helps you to analyze and understand the data distribution and correlation between different variables which can be used to improve your model. Matplotlib and Seaborn can be used for data visualization.

4. Feature Engineering — Select the best features to train your model. This includes transforming existing features or adding new ones to improve the model.

5. Model Selection — Select the model best suited for your needs. You can choose any of the above models to accurately predict the future selections.

6. Model Training — Split your data into training and test sets and use the most appropriate algorithm to train your recommender system.

7. Hyperparameter tuning — To optimize the performance of your model and to make it work better hyperparameters like learning rate, regularization strength, and number of hidden neural networks can be changed.

8. Model Evaluation — This is a crucial step to make sure that the recommender system is working accurately.

9. Deployment — On completion of all the above steps you can now deploy your model for your personal use or make it available to your friends, family, or even strangers on the internet.

Now you can rest assured that there are no spies around you who feed your information to the apps, but it is all the work of a recommender system.