(testing signal)

Tag: RecommenderSystems

Graph Neural Network (GNN) Architectures for Recommendation Systems

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Clustering types with various applications

Clustering types and their usage areas are explained with python implementation

Ibrahim Kovan

Unlabeled datasets can be grouped by considering their similar properties with the unsupervised learning technique. However, the point of view of these similar features is different in each algorithm. Unsupervised learning provides detailed information about the dataset as well as labeling the data.

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Understanding Graph Mining

Guide for Graph Mining Learning

Your first baby step to learn Deep Learning for Graph Network

Imagine Facebook: How do you get connected within layers of friends?

Imagine Recommendation System: How do you know a person’s preference is closely related to its clusters?

Welcome to Graph Mining

Graph classification generates graphs among a vast amount of connected data (e.g: Social, Biological, and Payment) and uses the graphs to identify labels (supervised) or clusters (unsupervised).

If this sounds tough to you, you can look no further than your brain because it is the master at inferencing connections among graphs quickly.

For example, your brain knows how to get from point A to point B without even thinking.

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Big Data Analytics: The Role it Plays in the Banking and Finance Sector

The finance industry generates a huge amount of data. Did you know big data in finance refers to the petabytes of structured and unstructured information that helps anticipate customer behaviors and create strategies that support banks and financial institutions? The structured information managed within an organization enables providing key decision-making insights. The unstructured information offers significant analytical opportunities across multiple sources leads that leads to increasing volumes.

The world generates a staggering 2.5 quintillion bytes of data every single day! Seeing the abundance of data we generate, most businesses are now seeking to use this data to their benefit, including the banking and finance sector.

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[Herald Interview] ‘Hiring philosophy is changing at South Korean companies’ – The Korea Herald

CEO of AI-based recruiting platform Wanted Lab on how digitization is reshaping Korea’s job market

CEO of Wanted Lab, Lee Bok-kee (Park Hyun-koo/The Korea Herald)

When Lee Bok-kee, founder and CEO of Wanted Labs, quit his high-paying consulting job in 2013, he had only one goal: to solve problems.

Having spent around five years at a consulting firm seeing tech mammoths like Samsung and LG pay millions of won to solve business problems, he was certain there would be at least one problem he can solve on his own.

But finding the right one was not an easy journey.

After brainstorming hundreds of businesses ideas, two failed startups, and losing his 100 million-won ($86,400) severance pay, Lee realized the problem he was looking for was right under his nose.

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Topic Model Based Recommendation Systems

A very quick and (hopefully) easy to follow introduction into the intuition (and very low level Maths) involved in Topic Model Based Recommendation Systems.

Check out my GitHub for a working simple recommendation system based on Topic Modelling.

In todays world, sometimes it feels like we are plagued with never ending decisions. Whether it be the Friday night movie or the next song to keep people dancing at an NYE party.

So how do recommendation systems actually work? In this article I’m going to explain one approach based on Topic Modelling using a Latent Dirichlet Allocation (LDA).

Topic Modelling

Before we talk about how to model a topic, we need to first understand what a topic actually is.

Photo by David Pupaza on Unsplash

This is not an intuitive idea to think about so we will describe it in terms of collections of words.

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How to Use Reinforcement Learning to Recommend Content

A new RL framework develop by researchers at Google.

Figure 1: the reinforcement learning framework. Image by author.
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Answering Causal Questions in AI

Two of the main techniques used in order to try to discover causal relationships are Graphical Methods (such as Knowledge Graphs and Bayesian Belief Networks) and Explainable AI. These two methods form in fact the basis of the Association level in the Causality Hierarchy (Figure 1), enabling us to answer questions such as: What different properties compose an entity and how are the different components related each other?

In case you are interested in finding out more about how Causality is used in Machine Learning, more information is available in my previous article: Causal Reasoning in Machine Learning.

Knowledge Graphs are a type of Graphical Technique commonly used in order to concisely store and retrieve related information from a large amount of data.

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Recommender Systems

Full implementation is complex: it needs advanced linear algebra.

Types of Recommender Systems:

  • Content based. Focus on the attributes of the items: the usual “related items”.
  • Collaborative filter (CF). Uses “wisdom of the crowd” to recommend items: eg Amazon. CF is most used on content based systems. It can do feature learning by itself.
    The Movie land dataset of movies to study.  These methods can be:
    – Memory based CF: singular value decomposition.
    – Collaborative CF: computing cosine similarity.