Adam Powell and Denis Dallinga - Recommendation systems @ Catawiki

published May 13, 2016, last modified May 17, 2016

Adam Powell and Denis Dallinga talk about recommendation systems at Catawiki, at PyGrunn.

See the PyGrunn website for more info about this one-day Python conference in Groningen, The Netherlands.

Catawiki is an online auction platform, for all kinds of things, including Napoleon's hair. Some projects ar interesting programmatically.

Auction listing page optimisations. Which options do we recommend to users? We base this on similar users. We can use a Jaccard normalised model. The Co-Occurrence model gives different recommendations.

Bids go into the data warehouse, to Spark, to the ruby 'grape' framework which is a personalisation service. We A/B test new ways of doing recommendations. We need to balance popularity and novelty, freshness (don't keep showing the same ones), diversity.

Problem: new users of which we don't yet know much. We use the Python library theano [see the machine learning talk, Maurits].

We have 35 thousand auction lots per week, and 350 thousand bidders, which leads to a lot of data. Recommendations for all users can be recalculated every five minutes. Using Snowplow for recommendations to all users.

Recommend a category for a new lot that someone enters. gensim Python module for natural language processing, run this over all categories, put this in elastic search.