Where I express personal thoughts in words
Various properties are commonly considered when choosing the recommendation approach, whether for offline or online scenarios. These properties have trade-offs, so it is critical to understand and evaluate their effects on the overall performance and the user experience. This blog post is my attempt to summarize these properties succinctly.
In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for my Master Thesis. Part 7 explores the use of Boltzmann Machines for collaborative filtering. More specifically, I will dissect three principled papers that incorporate Boltzmann Machines into their recommendation architecture. But first, let’s walk through a primer on Boltzmann Machine and its variants.
In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for my Master Thesis. In Part 6, I explore the use of Auto-Encoders for collaborative filtering. More specifically, I will dissect six principled papers that incorporate Auto-Encoders into their recommendation architecture.
In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for my Master Thesis. Part 4 looks into the nitty-gritty mathematical details of matrix factorization, arguably the most common baseline model for recommendation system research these days.
In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for Master Thesis. Part 3 addresses the limitations of using deep learning-based recommendation models by proposing a couple of research directions that might be relevant for the recommendation system scholar community.
In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for my Master Thesis. Part 2 provides a nice review of the ongoing research initiatives with regard to the strengths, weaknesses, and application scenarios of these models.
In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for Master Thesis. Part 1 provides a high-level overview of recommendation systems, how they are built, and how they can be used to improve businesses across industries.
