© 2023 Hashnode
#recommender-systems
PYTORCH x MEMGRAPH x GNN = 💟 Over the course of the last few months, we at Memgraph have been working on something that we believe could be helpful with classical graph prediction tasks. With our lat…
We held a company-wide hackathon where we challenged each other to build compelling, useful applications using a streaming data source, Kafka, Memgraph, and a Web Application backend. This week we're …
Recommendation systems are a type of machine learning algorithm that is designed to predict what a user might be interested in and present them with personalized recommendations. These systems are com…
Music plays an integral role in our life right. It's there to lift our moods whenever we have a "Bad Day" (Song by David Powter btw) or when we need to just chill and code like in our case. So without…
Explicit v.s. Implicit ratings Two ways to gather user preference data to recommend items, Ask for explicit ratings from a user, on a rating scale (such as rating a movie from one to five stars). The…
Online businesses often face the challenge of how to effectively personalize their products and services for individual users. One way to do this is to use machine learning to analyze user behaviour a…
Introduction You must've visited websites like Amazon, Netflix, and Prime Video and would've noticed that there is a separate recommendation section on these websites as shown in the images below. T…
Getting Started In this tutorial, we’ll build a simple machine learning pipeline using a BERT word embedding model and the Nearest Neighbor algorithm to recommend wines based on user inputted preferen…
In recommender systems, we typically work with very sparse matrices as the item universe is very large while a single user typically interacts with a very small subset of the item universe. Take YouTu…
