March 5, 2022
Welcome back to part three of the series on creating recommendations with the H&M dataset. To recap on the previous posts, here are the links.
February 23, 2022
For the uninitiated, here’s a quick recap on the previous parts of this series:
February 17, 2022
So in the past couple of weeks a Kaggle challenge has been opened up with the task of creating clothing recommendations based on a dataset from H&M. The data consists of images of all the items, user info and item info. Over the course of the next few weeks I’ll be looking at how we can combine multiple different methodologies to create insightful recommendations. I’ll start in this post by loading the data into a Neo4j database and doing some exploratory data analysis and in subsequent posts look at how we can use graph databases for recommendations and how we can incorporate the image data using image embeddings from pre-trained neural networks.
February 10, 2022
So I thought I’d do a post for those interested in rugby this week and look at how we can model the number of tries scored per team per game. I’ll look at fetching the data from a database, processing the data and finally what sort of distribution we can use to best model the data. Let’s start with accessing the database.
January 27, 2022
To recap last weeks post, we finished by extracting all the named entities from the research paper abstracts and writing them to our graph. A question from Mark Fallu (@brisvegas1) on Twitter after part 1 of this series was interested if we could align graph embeddings with document embeddings so that’s what we’ll explore this week.
January 20, 2022
In last weeks post, we loaded up a graph with research papers and the associated authors and institutions. This week we’ll look at a few different things:
