AI News, Building Data Products with Python: Using Machine Learning to Provide Recommendations

Building Data Products with Python: Using Machine Learning to Provide Recommendations

This is the third part of our tutorial on how to build a web-based wine review and recommendation system using Python technologies such as Django, Pandas, SciPy, and Scikit-learn.

Then, in the second section, we will use a naive recommendation criteria so we can better concentrate on building the views and models needed to provide recommendations.

We will finish the tutorial by using K-means clustering as a machine learning model that makes use of user similarity in order to provide better wine recommendations.

Remember that you can follow the tutorial at any development stage by forking the repo into your own GitHub account, and then cloning it into your workspace and checking out the appropriate tag.

We have a review ID, a name for the user making this review, a wine id (that cross-links with the file wine.csv), a rating, and a comment.

Put the three scripts at the root of the project (side by side with and the data files in a folder named data under the project root.

But first let's add a URL mapping in reviews/ so it looks as looks in the following GitHub file at stage-2.2 (we are having problems rendering this code snipped here with the tutorial right now).

element in templates/base.html to look like the following (if you don't see the html tags rendered in the following snippet, have a look at the GitHub repo file at stage-2.2).

It will be the equivalent to a totally ignorant wine store clerk who, when asked to recommend a wine for tonight's dinner, first inquires what have we tried and then suggests something with a different name without really considering the wine style or our taste.

We also prefetch wine objects in order to avoid successive queries for each review, since we will need to access these wine objects (more about prefetch_related).

In the first approach, we ask her to tell us a few wines she liked and, based on our knowledge about these wines, we recommend her wines that are similar.

It is what a good and experienced wine store owner would do, and suggestions are hard to improve by a computer that follows the same approach due to many unknown factors that are involved in this kind of customer relationship.

Then we ask that second person for her favourite wines and suggest them to our first friend, not including those that our first friend have already tried.

Instead of trying to compare our user to every other user in the system every time recommendations are needed, we will pre-cluster all the users in the system by its wine reviews scores.

Since we know all the users in that cluster have a similar taste (they scored similarly the same wines, for good or bad), this greatly reduces the search space.

collaborative filtering using ALS), but this one is easy to understand in terms of its meaning and makes use of some machine learning techniques we already know about.

If we assume the cluster ID has been previously assigned by our clustering algorithm, this whole process will give us wine suggestions that satisfy two conditions: We could store this together with the rest of the user's information, but in order to do that we would need to provide a custom user object.

In addition, if we do so we'd need to reconstruct all the database tables related to authentication (this process doesn't support migrations) and this is a major restructuration of our system.

and And although it won't be the main tool to manage cluster information, we are goind to add the model class to our admin interface.

We have just imported and registered the model class Cluster, and associated a ClusterAdmin class that will better visualise cluster information (name and members) in the admin interface.

So, go to the admin interface and create three clusters with the following members from the users we have available: If you have problems with this, just checkout the tag for this stage of the project that is stage-2.4 and contains all the previous code and the information in the database.

We just need to tweak the view layer a little in order to decide when to perform the k-means clustering, and then we need to write the actual clustering code.

Let's put at least a couple of restrictions: The first restriction can be satisfied if we call for cluster assignments update within the view that handles adding wine reviews.

The update_clusters function performs cluster assignment in three steps, and only updates if the number of total reviews in the system satisfies a certain equation (more on that later on): We are almost there.

Go and edit reviews/ so the method user_recommendation_list looks like the following: Basically, we have added a try-except clause in order to deal with non existing cluster assignments for a user when getting the first cluster.

Either you check out the latest tag in the repo stage-3 - and in that case you will have the data in the database already - or you get (even copy and paste will work) that individual file to replace the previous data/reviews/csv.

Then we used K-means clustering as a machine learning model that made use of user similarity in order to provide better wine recommendations.

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