Prediggo Technology

Our Technology

Research & Innovation

Stemming from the scientific research in Artificial Intelligence at EPFL, Prediggo has developed a unique technology to increase eCommerce sales.

Our search engine

Unique algorithms

At Prediggo, we do not use an open-source engine, but have developed our own technology.

Improved relevance

We do not use the basic version of the Levenshtein algorithm to calculate distances in case of error, but the advanced version called Oflazer. Levenshtein’s version works well for the English language but is not well adapted to European languages because it does not discriminate errors according to their typology. ith Levenshtein’s algorithm, changing an “e” into an “x” is worth 1 point; this is the same distance as changing “e” into “é”. Oflazer’s algorithm allows us to apply different weightings according to the type of error and thus offer more relevant results for European languages.

Deep Learning

Product sorting does not depend solely on document classification algorithms such as TF-IDF and the weighting of particular attributes. Prediggo combines these algorithms with eCommerce business KPIs (like click-through rate, conversion rate, sales…) in order to offer a self-learning algorithm that improves search quality while increasing site conversion.

More powerful autocorrection

Lucene only uses the Levenshtein algorithm to correct spelling mistakes. At Prediggo, we use a combination of different algorithms like Oflazer, phonetic correction, Word split and many more. Our Genetic Search algorithm is based on the DNA genome, where each search algorithm is modelled on a DNA nucleotide, and the correction algorithm sequence is unique to each customer in order to adapt to the errors made by their end user customers.

Ease of use

Lucene’s configuration can be very complex and requires advanced knowledge in the field of document retrieval, which is not really compatible with the eMerchandising world. With Prediggo, any person with the relevant rights can easily change the tool’s settings from the Prediggo Cockpit.

Our recommendation technology

Unique algorithms

The patented Ontology Filtering algorithm differs from current algorithms in several crucial aspects:

Improved relevance

The algorithm does not only use sales data but also takes into consideration semantic product information (such as product attributes). This allows us to better understand what people are buying and to know what intrinsic features of the product are important to the Internet user.

Better relationship modelling

Product sorting does not depend solely on document classification algorithms such as TF-IDF and the weighting of particular attributes. Prediggo combines these algorithms with eCommerce business KPIs (like click-through rate, conversion rate, sales…) in order to offer a self-learning algorithm that improves search quality while increasing site conversion.

5X less data

Our engine is able to make recommendations with 5 times less data.

Adapted to your activity

The semantic layer makes it easy for the eMerchandiser to add business constraints in order to gear the recommendations towards strategic business objectives.

Transparent

Ontology Filtering is not a black box, and the recommendations can be explained quite easily to a human being.

Research & Development

A true scientific partnership

Since it was founded in 2008, Prediggo has been using a patented technology developed in the Artificial Intelligence Laboratory (LIA) of the Swiss Federal Institute of Technology Lausanne (EPFL).

Context Tree, a new algorithm

From the beginning, Prediggo has built a close partnership with the EPFL, which has allowed us to develop new, avant-garde solutions for e-tailers. This collaboration produced the new Genetic Search search algorithm, and more recently the new Context Tree recommendation algorithm.

The Context Tree is a new artificial intelligence method that uses Bayesian Variable-order Markov Models based on users’ journeys on the site in order to make recommendations. AB tests at our customers’ sites have shown that this new algorithm, when given enough customer journey data, sells up to 4 times more products than traditional solutions. Without these data, the Ontology Filtering algorithm remains the best.

These results were published at theAAAI 2019 conference, one of the most prestigious Artificial Intelligence academic conferences in the world. Many resellers talk about Artificial Intelligence without publishing their research or having it validated by independent researchers. At Prediggo, we are proud to demonstrate our competence in this field, and help the scientific community to move forward.

Boi Faltings “In addition to my research and teaching at EPFL, I serve and have served the Artificial Intelligence community as Associate Editor of various academic journals. I regularly serve on conference organizing committees (IJCAI, AAAI, ECAI, and others) and have been a program (co-)chair for seminars and conferences.”

“Through our work with Prediggo, we have a better understanding of the impacts and limitations of our research technologies, which allows us to better guide them. Few research groups have the opportunity to measure the impact of their algorithms in real-life conditions. We hope to continue this strong partnership in the future.”

Boi Faltings

Professor of Artificial Intelligence, École Polytechnique Fédérale de Lausanne (EPFL)

Prizes awarded to Prediggo