Well, well! Looks like I'm about to embark on a project where I can put all those tips and tricks I've gathered from my years in the software development industry to good use. But hey, before I dive into the coding frenzy, I want to create a core system that's not just your ordinary run-of-the-mill code. Nope, this bad boy needs to be as flexible as a gymnast doing yoga while juggling oranges 🤸♀️🍊. Who knows, this core system might just become the superhero of code that can be inherited by other projects, saving me some precious time for a little meditation 🧘♂️.
Before starting any project, We as a software developer should focus on understanding core business value it can give the consumers/users/customers. Since software developer are also human, they cant do both coding and requirement analysis of what customers really want so to avoid these bottleneck and make this pet project minimal. we should be focusing on all the each component, tools, practices on the surface level.
Whenever we embark on a new project, it is a best practice in software development to start by designing the software architecture diagrams. There are a lot of tool draw.io, Windows Paint you can use. For this application I have used draw.io.
So I finally name the project as recommendation system. Core functionality of this pet project will be focusing on recommendation side. So with the help of nursery draw, I can communicate my ideas or thought process well with my team.
So brief intro to this services:
Data collection service : In this service we will be adding feature that are related to gather information about our end user such as their demographic information, preferences, behavior, and any other relevant data points.
Content Curation service: Curate a diverse set of content that aligns with the interests and needs of our target audience. This can include articles, videos, podcasts, courses, or any other form of content that provides value and addresses different aspects of users' lives. For now I will be only focus on news content.
User Profiling service: Develop a user profiling mechanism to capture users' personalities, preferences, and goals. This can be achieved through various techniques, such as analyzing user behavior, survey responses, or employing machine learning algorithms to infer user characteristics.
Machine Learning Algorithms: Implement machine learning algorithms to analyze user data and content attributes. You can use techniques like collaborative filtering, content-based filtering, or hybrid approaches to generate personalized recommendations.
Feedback Loop and Learning : Establish a feedback loop to capture user feedback and interactions with the recommended content. Incorporate mechanisms for users to rate, like, or provide feedback on the recommendations. Use this feedback to continuously improve the recommendation algorithm and enhance the personalization capabilities.
Email Personalization: Once we have a solid recommendation system in place, leverage personalized emails to deliver curated content to users. Craft compelling email templates that highlight the recommended content based on user preferences and goals. Tailor the messaging to resonate with different user segments, such as children, teenagers, or adults in different life stages.
