Can you walk us through how you would build a recommendation system?

 he type of recommendation system (RS) that is supposed to be built depends on the size of the current user (and product/item) base of the application.

RSs have a "cold-start" problem, i.e., if we have a low amount of data then they aren't effective at all. But, once we gather enough data points, we can utilise them to provide recommendations to our users.

Therefore, to solve the cold-start problem I would suggest a popularity-based algorithm. A popularity-based algorithm ranks all items by some metric (e.g., purchase count, number of views, etc.) and recommends the items that rank at the top. It is very obvious that this approach over-fits on the most popular items, and always recommends them, but when we have a low number of items it makes sense.

Once the user base grows, and we have gathered some amount of data, we can apply a more advanced algorithm for recommending items. The two most popular approaches are:

  1. Item-based filtering: to each user we recommend the items that are most similar to his purchase history
  2. User-based filtering: to each user we recommend the items that are most frequently bought by users that are most similar to him

In both cases, the measure of similarity needs to be defined and is application-specific. In practice, the two approaches are often combined to provide a hybrid RS.

Yet another approach to building an RS is through a classification model. A classification model would take as input user- and item-related features and output a probability/label for each item that would represent the likelihood that the user would buy that item. The model can be any classification model: logistic regression, KNN, neural network, etc. I would imagine that this is the approach that the big companies (like Amazon, Google, Netflix) implement to provide personalised recommendations.

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