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SL O-Horizon Recommendation Engine

Overview

The SL O-Horizon Recommendation Engine was developed as part of the project titled "SL O-Horizon: Enhancing Discovery On Beckn Enabled Open Networks Through Recommendations Using Neo4j & GDS". It was created during Skill-A-Thon 1.0, which was a hackathon organized by the EkStep Foundation and the DSEP (Decentralized Skilling and Education Protocol) community.

SL O-Horizon project emerged as the winner in the challenge 4 category of Skill-A-Thon, which focused on "Unlock opportunities for the next billion by building intelligent technology solutions to find and deliver services". Subsequently, the project and the engine was featured in Episode 2 of Neo4j's hour-long live-stream series called Graphversation.

Neo4j Graphversation Ep. 2 (Recommendation Engine in Ed-tech Using Neo4j & GDS): Watch here (Youtube)

This repository serves as the reference for the SL O-Horizon Recommendation Engine, aligning with the contents discussed in the Graphversation episode.

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Workflows Supported

  • Collaborative filtering based recommendations i.e., recommendations based on user to user similarity.

    Scenario Supported: Item recommendations for a user based on how similar they are to other users and their preferred items. (Input: UserId)

    GDS Algorithms used: Fast Random Projection Embedding (FRP) & K-Nearest Neighbors (KNN)

  • Content based filtering recommendations i.e., recommendations based on similarities between the contents of the items.

    Scenarios Supported:

    • Item details page recommendations: Recommend items which are similar to the selected item. (Input: ItemId)
    • Profile page recommendations: Recommend items which are similar to the items previously rated by the user. (Input: UserId)

    GDS Algorithms used: Node Similarity

  • Smart Auto-search: Using this workflow, the recommendation engine identifies the relevant topics in the graph database. And it will make search queries to an external service (SL O-Horizon BAP) on those topics. Which will result in recommendation engine getting enriched with newer items as time passes, thus self-improving the recommendations for the users.

    GDS Algorithms used: PageRank & Node Similarity

    Note: Since "Smart Auto-Search" wasn't mentioned during the Graphversation Episode, use this article as the reference for the working, architecture & feature walkthrough: Leveraging “Smart Auto-Search” On O-Horizon Recommendation Engine

Additional Features

  • Synthetic data generation scripts: The dataset demonstrated during Graphversation can be generated with a single command. Graph
  • Swagger Api-doc: This repository comes with a fully fledged swagger api-doc which can be accessed from the web-browser. Swagger
  • Postman collection: Postman collection with all APIs are included with this repository.

Prerequisites

  1. Node.js 18
  2. Docker Compose
  3. Neo4j GraphDB & GDS (Included in docker-compose file)

Setup Guide

  1. Install Node.js & Docker Compose.

  2. Clone the repo and run "npm i" command from within the src directory.

    o-horizon-recommendation/src$ npm i

  3. Now run the 'docker-compose up' command from the root directory.

    o-horizon-recommendation$ docker-compose up

  4. Set unique constraints for the graph database (graphDB) by running the following command from the src directory:

    o-horizon-recommendation/src$ npm run set-unique-constraints

  5. [Optional] To replicate the same dataset used during Graphversation, run:

    o-horizon-recommendation/src$ npm run graph-seeder

  6. [Optional] To access swagger api-docs, visit the following url from the web-browser.

    http://localhost:3020/recommendation/api-docs/

  7. [Optional] Import api collection from src/apidocs into postman.

    o-horizon-recommendation/src/apidocs/O-Horizon.postman_collection.json

  8. [Optional] To access Neo4j browser, visit the following url from the web-browser. (Login without username & password)

    http://localhost:7474/browser

Additional References