The easiest way to enable the product recommendation section is to use one of the official themes from Shopify: (Boundless, Brooklyn, Debut, Express, Minimal, Narrative, Simple, Venture).
There are also third-party themes that support product recommendations.
If you are not using any of the two options mentioned above, it will be hard to get the product recommendations to work. To make things more confusing, Shopify came…
We have recently completed a major migration of our database technology from Hbase (part of the Hadoop stack) to Apache Cassandra.
One of the original appeals for selecting Cassandra is AWS Keyspace since Siggy runs entirely on AWS infrastructure.
However, after testing our Siggy on AWS keyspace, we discovered the following reason for staying away from it:
We use count query to fetch index status from the database which is a common use case, however, this is not supported by AWS at the moment
With the launch of Siggy, our AI-based product recommendation app for Shopify. I thought it would be a good time to share the product journey from the background motivation to any of the future plans.
I was working on Trakr, a visual QA automation product where we were running into the problem of needing to classify elements on websites to improve the testing accuracy.
Automated visual testing platformwww.trakr.tech
It quickly became clear to me that any rule-based system will not be able to classify the components of a website quickly, easily, and accurately.
Since Trakr relied entirely on taking screenshots of websites to perform…
As our product recommendation engine (Siggy) matures, we are starting to look at areas that can improve its performance. The two areas of performance we are looking at improving are:
The goal will be to reduce the time needed to “encode” each image as well as reduce the size of encoding for each image.
For this blog post, we will discuss our investigation into reducing the storage size of the encoding.
Siggy uses a “content-based” recommendation algorithm. This means that we only need “content data” for the algorithm to work.
In the context of a product recommender, we only need product attributes such as (Product name, description, images, tags) to generate effective recommendations for the user.
There are three primary reasons we choose a “content-based” recommendation algorithm first:
This is the classic dilemma of building digital products from experience and proven technology vs. trying new things.
This has been a major theme in the past few weeks as we struggle to bring the product to its final stages for testing.
One of the changes I was keen on making to our queue system in Siggy was making…
We are almost 3-months into the venture of building a functional prototype for our recommendation engine and we are almost there!
Our goals for the functional prototype are still:
Additionally, the ability to demonstrate the value with KPMs will be important!
Moving closer to our goals also means we building less and refine more. It is both a tedious and also important process.
Improvements in recommendation
One of the keys to an effective working day or week is to group related tasks together where you can concentrate and make great in-roads. Last week was not one of those weeks.
For example, we had planned various types of tasks:
At the end of the week…