How do you deal with data sparsity and scalability issues in your recommender system?

So, matrix factorization helps the platform overcome the challenge of not having enough data about your movie preferences. By breaking down the user-item matrix into smaller pieces and capturing latent factors, it can make accurate recommendations even when there are missing ratings.

2) However, since you haven't watched or rated all the movies, most of the cells in the table are empty or missing. This is called data sparsity because there's not enough information available for all the movies and users. 3) To overcome this problem, the platform uses matrix factorization. It breaks down the user-item matrix into smaller pieces, which represent different aspects or characteristics of movies and users. These pieces are like puzzle parts that help understand your movie preferences.

6) With these representations, the platform can predict how much you might enjoy a movie you haven't seen yet. It combines your preferences (action, excitement) with the characteristics of the movie (comedy, adventure) to make an educated guess. 7) Based on these predictions, the platform suggests movies that it thinks you'll like, even if you haven't watched or rated them before. This is how it personalizes the recommendations to your unique taste.

4) For example, let's say the platform discovers that you enjoy movies with lots of action and excitement. It finds a way to represent this preference as a small set of numbers, like [0.8, 0.2]. These numbers capture the action factor and excitement factor that influence your movie choices. 5) Similarly, the platform looks at each movie and identifies its characteristics or factors, like comedy, adventure, or romance. It also represents these characteristics using small sets of numbers. For instance, a comedy movie might have the characteristics [0.9, 0.1], where the first number represents how funny it is, and the second number represents how much it's a romantic comedy.

Let's assume there's a popular streaming platform that recommends movies to its users based on their preferences. However, since there are thousands of movies available, it's impossible for the platform to know exactly which movies you'll like. To help with this problem, the platform uses matrix factorization, that helps it understand your tastes and make better movie recommendations for you. Here's how it works: The platform creates a big table called a user-item matrix, where each row represents a user (like you) and each column represents a movie. In this table, each cell contains a number that shows how much you liked or rated a particular movie.

I've used PCA (principle components analysis) to create product dimensions/themes based on high loading products on top (above the bend) factors. Those become your next best product recommendation groups.

Sparsity-related Metrics: a. Sparsity Ratio: This metric calculates the percentage of missing values (unobserved interactions) in the user-item interaction matrix. A higher sparsity ratio indicates a more sparse dataset. b. Coverage: Coverage measures the percentage of items for which the recommender system can provide recommendations. It indicates how well the system handles the sparsity issue by making suggestions for a larger portion of items. c. Mean Interactions: This metric determines the average number of interactions (ratings, clicks, etc.) per user. A low mean interactions value indicates data sparsity.

Scalability-related Metrics: a. Training Time: Training time measures how long it takes for the recommender system to learn from the data and generate recommendations. A shorter training time indicates better scalability. b. Prediction Time: Prediction time measures how quickly the recommender system can generate recommendations for a user once it's trained. Lower prediction time is desirable for scalability. c. Memory Usage: Memory usage quantifies the amount of memory required by the recommender system to store and process the data. Lower memory usage is preferred for scalability.

-typically many ecommerce spaces are using reinforcement learning, especially MABs, which comprise a large chunk of recommender systems. DNNs are mentioned above, but those can also be an input to a MAB via contextualization.

Sparsity can sometimes be reduced by getting a bit more creative with inputs. It's often simplest to lean on explicit scoring or binary outcomes (purchases, clicks, upvotes), but there could be data pointing to partial or implicit scoring. Something like a medium dwell time, multiple page views or any equivalent where the journey gets going but may fall just short of conversion. There's nuance here - some domains might consider a partial journey as a moderate success and for other cases it's worse than a complete skip/miss, but it's worth seeing if you can cast the net a bit wider for inputs. These then wouldn't be a 1 or a 0, but perhaps a 0.3 or even a -1 depending on context. It's not quite Feature Engineering but certainly adjacent.