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How Deep Learning Search Works
How e-Commerce site-search works

Deep dive into Machine Learning

Peek behind the curtain to learn the magic of Loop54

What is Machine Learning?

Loop54's advanced eCommerce Machine Learning algorithm takes us one step closer to Artificial Intelligence. It is based on a set of algorithms that attempt to model high-level abstractions in data using a deep architecture with multiple processing layers and that permit software to train itself to perform tasks.

It starts with a product feed

We begin by building a model of the product catalogue and training our algorithm on the model


A model capable of learning

Once complete, the model resembles the central nervous system of a human brain. It encompasses a multi-layer system of interconnected "neurons".

Each neuron in the model has a set of numeric feature weights that adjust based on experience (i.e. new inputs). This makes the model capable of learning.

The features that are weighted for each neuron represent all the brands, categories and product attributes found in the catalogue metadata.

Machine Learning eCommerce

Product-neuron matching

During training, we loop all products through the model. Each product input will match one neuron, and both the product and neuron will remember that match forever. 

Like a neuron, each product is made up of a set of feature weights. Therefore, we locate a matching neuron by comparing its feature weights to that of the product input. 

A match occurs when the product and the neuron have the same, or very similar, feature weights.

Machine Learning eCommerce

The first search is made

At the first search, we use a unique combination of text-matching and Machine Learning to present a distributed sample of relevant results. As we gather behavioural data we refine the results for greater relevance.

Basic text-matching

When the first search is made, we do not have any behavioural data yet. Instead, we rely on basic text-matching to produce a list of products that contain the search query in their metadata.

Each product in the list is given a score based on where that word is located in the metadata and how frequently it is found (i.e. higher score when word is found in the product title versus the description).

Before giving the visitor search results, the list of products found through text-matching is passed over to our Machine Learning algorithm for further analysis. 

Machine Learning eCommerce

Matched neurons

Our Machine Learning algorithm takes the list found through text-matching and locates the neurons that match the products in the list.

If all the products in the list don't match the same neuron, or if the matched neurons are not from the same cluster (i.e. clusters of neurons share similar feature weights), then our algorithm will divide the list up into groups of products that match the same neuron and/or neuron cluster. 

List groups:
Group 1: Yellow pot., Russet pot., potato flour ...
Group 2: Yukon french fries, yams ...
Group 3: Stir fry, croquettes ...

Machine Learning eCommerce

Random sampling

We then take a random sample of products from each of the newly created groups and present them as results to the visitor. 

The sample distribution and ranking of results are set by the text-matching scores (i.e. larger samples will be taken from the highest scoring groups).

This allows our engine to present the visitor with a range of relevant direct results, without letting one specific category or product type dominate.

Probability distribution:
Group 1: 37%
Group 2: 22%
Group 3: 10%

Deep Learning search algorithm

Behavioural data

As soon as a visitor interacts with the search results (e.g. clicks, add-to-cart, purchases), we can begin to refine results according to behavioural data. 

So the next time a visitor searches with the same or similar query, past behaviour will determine which products best represent the intended meaning of the query.

Over time, we will no longer rely on text-matching for the list of products our Machine Learning algorithm will process. Instead, we will refer entirely to past visitor behaviour. 

Updated probability distribution (using behavioural data):
Group 1: 68%
Group 2: 15%
Group 3: 5%

Eventually, there will be enough behavioural data that a random sampling of products will no longer be needed to establish relavance.

Machine Learning eCommerce

The relevance of related results

In addition to direct results, our engine generates a list of related results. These results have no connection to the actual search query, but are relevant because their feature weights resemble those of the direct results.


Products and neurons

As stated above, when a product and neuron have identical feature weights they are considered a match.

While each product can only match one neuron, each neuron can match several products. 

The products that match the same neurons as the products found through text-matching and/or behavioural analysis are what supply the list of related results.

Related and Recommended Search Results

Related results

The products listed under related results are similar to the products listed under direct results.

They are similar because they match the same neurons and therefore have similar feature weights. 

These results often relate to the direct results in one of three ways:

  1. Brands with similar product lines
  2. Products with similar uses
  3. Similar products from other brands
Loop54 Related Results

Watch demo videos!

We've recorded a series of short demo videos that exemplify each of the concepts presented above. See how we personalise, auto-merchandise, learn words, handle misspellings, and more!