Welcome to Post 3 in a series of posts on Data Science – Recommendation Engines.
In this post, we discuss an important aspect one should remember always while learning Recommendation Engines (RE). For this reason, this post is intentionally kept short.
Whenever you are learning RE, establish below idea at the back of your mind.
Recommendation Engines means:
Users “searching” for content -> Your RE model should provide “relevant” results -> Based on “Taste”
Simply put, “Search -> Relevance -> Taste“
To achieve above, identify the popularity of “Items” based on either current user history with your website or/and other similar users’ history.
For example, “Navigation Visits” of current or other users can be used to provide the “Relevant” search results/recommendations.
Here are the links to previous posts Part 01 Part 02
- The AI Blueprint: Decoding “Latent Space” in Latent Diffusion Models (LDMs)
- NLP Made Simple: The 12 Core Tasks Explained in One Table
- AI LLM Learnings – Understanding Words, Sentences, and Tokens – Using a Real Example from AWS EC2 Documentation
- Agentic AI Design Patterns for DevOps in Cloud-Native Kubernetes Environments
- AI – Building a Real-Time FOREX Position Risk Manager with VLLM, AWS, and Bedrock
- Machine Learning Experiences: Enhancing Synthetic Trade Data for FOREX Risk Modeling
- Machine Learning Mastery – Understanding Ensemble Methods And Their Benefits
- Chief Architect Decision Making – Matrix for AI in Forex Risk Ops
- Chief Architect AI Lens – Enterprise AI Blueprint for Forex Position Risk Operations