The goal of this projet is to recommend books / articles to customers, integrate in a Node.js Mobile App.
News Portal provided by Globo.com, one of the most popular media company in Brazil.
More information available on Gabriel Moreira's Github/paper
In this project, we have IMPLICIT data, i.e. we don't have clear (explicit) feedback from the users about their preferences, like articles' rating.
Besides, there are additional drawbacks:
- We don't have full data about interactions: it would have been interesting to have at least the view duration of an article;
- We don't have the details of articles for confidentiality reasons: it's difficult to see "visually" if the recommendations are really relevant.
- ✔️ Perform Exploratory Data Analysis (EDA);
- ✔️ Try different RecSys model (by popularity, Content-Based, Collaborative Filtering);
- ✔️ Select the architecture to meet the business need;
- ✔️ Integrate in a Node.js Mobile App;
- ✔️ Deploy Content-based RecSys on Azure (Azure Functions, Azure Blob Storage).
Pandas, sklearn, implicit library, Azure Functions, Azure Blob Storage, Github, VS Code
- Fork of Bookshelf App on Github;
- Node.js, including npm (Node package manager);
- Azure Functions Core Tools;
- Android Studio.
- Recommendations systems: Categories of RecSys
- Implicit / Explicit data :
- Cosine similarity : Sklearn pairwise metrics;
- Implicit library;
- Create Azure Functions with Visual Studio Code;
- Serverless : What is Serverless?
- ☑️ Check if the articles have been seen more than once by an user;
- ☑️ Try dataset with explicit feedback;
- ☑️ Use scikit-surprise library;
- ☑️ what about hybrid and neural networks RecSys?