Hey there, fellow learners! I’m excited to dive into the world of content-based recommendation systems. Recently, I came across a question on Reddit that sparked my curiosity. The user asked if it’s possible to build a content-based recommendation system from a CSV file that looks like this:
url;tags;score
For example:
url1;tag1 tag2 tag3;120
url2;tag2 tag5;50
or even (random topic):
some_image_url;fantasy-art medieval;250
The score is just the total upvotes on the image, and the tags can be nonsense words since users create them.
As a beginner, I was stuck, but I didn’t give up. After some research, I found out that it’s indeed possible to build a content-based recommendation system from such a CSV file.
The idea is to use the tags as features to represent each item (in this case, URLs or images). Then, we can calculate the similarity between items based on their tag features. Finally, we can recommend items to users based on their past interactions or preferences.
One way to approach this is by using a technique called collaborative filtering. We can create a matrix where each row represents an item, and each column represents a tag. The cell at row i and column j would contain a 1 if item i has tag j, and 0 otherwise.
From there, we can use dimensionality reduction techniques like PCA or t-SNE to reduce the dimensionality of the matrix and make it easier to compute similarities.
Of course, there are many other ways to build a content-based recommendation system, but this gives you an idea of how to get started.
What do you think? Have you worked on a similar project? Share your experiences in the comments below!