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The Loop54 Search Algorithm

From Topic Modelling to Neural Networks


Evolution of the Loop54 algorithm


Algorithm version Context model Description

Topic Modeling.png

Topic Model

Topic Modelling 

Determined underlying structure by clustering words that are used in the same context. Created initial ability to find Related Results.

 Batman New.pngBatman
(Q2 2013)

Behaviour Augmented Topic Model and Association Network (Topic Modelling) 

Added the inherent product structure to reinforce topic models. Improved contextual word clustering. Added behaviour-based learning, faceting, initial personalisation.  

 Coffee Bean Logo.pngBean
(Q1 2015)


Behaviour Enhanced Association Network (Graph Theory)

Created new ways of representing words/strings. Made it easier to configure and troubleshoot engine. Improved reliability of learning capabilities. Reduced algorithm training time by 90%.


(Q3 2016)


Generalized Organisation by Layered Expanding Maps
(Neural Networks)

Creates an alternative representation of the product catalogue structure - analogous to the structure of a well merchandised physical store. Increased conversion rate by 8% compared to Bean.

   Language model  

Language Model
(Q1 2015)

 Natural Language Processing (NLP)

Gives more efficient data structure and allows us to develop NLP techniques in a more flexible and scalable way. Greatly improved spelling corrections, compounded words, etc.