🚀 Building

AI-Powered Student Recommendation Engine

Students struggle to find relevant programs and services, leading to lower engagement and missed opportunities. Current recommendation system is basic keyword matching that misses contextual relevance and student preferences.

ACTIVE• Auto-managed
1/8/2024 - 2/19/2024
Timeline
0 days left
600 of 42 days
Scopes
1 of 3
33% complete
Appetite
42 days
Original estimate
Circuit Breaker
None
On track

Hill Chart

Drag scopes to show progress. Left side = figuring things out, right side = executing.

Problem SolvingExecuting SolutionPeakUser Profile Data Pipeline100%ML Recommendation Engine60%Recommendation API & Frontend20%

Scopes

User Profile Data Pipeline
ML Recommendation Engine
Recommendation API & Frontend
Planning
Active
Done
Drag scopes to update progress

Scope Details

User Profile Data Pipeline
DONEExecuting Solution
Must Haves
  • •Connect to existing user database
  • •Extract relevant profile attributes
  • •Data validation and cleaning pipeline
Nice to Haves
  • ~Real-time data sync
  • ~Advanced data quality metrics
ML Recommendation Engine
ACTIVEExecuting Solution
Must Haves
  • •Collaborative filtering algorithm
  • •Content-based recommendations
  • •A/B testing framework integration
Nice to Haves
  • ~Real-time model updates
  • ~Advanced similarity metrics
  • ~Bias detection tools
Recommendation API & Frontend
PLANNINGProblem Solving
Must Haves
  • •REST API endpoints for recommendations
  • •Student portal UI integration
  • •Basic analytics tracking
Nice to Haves
  • ~GraphQL API support
  • ~Advanced recommendation explanations
  • ~Detailed click analytics

Scope Management

User Profile Data Pipeline

Hill: 100%
•Must Haves (3)
  • Connect to existing user database
  • Extract relevant profile attributes
  • Data validation and cleaning pipeline
~Nice to Haves (2)
  • Real-time data sync
  • Advanced data quality metrics

ML Recommendation Engine

Hill: 60%
•Must Haves (3)
  • Collaborative filtering algorithm
  • Content-based recommendations
  • A/B testing framework integration
~Nice to Haves (3)
  • Real-time model updates
  • Advanced similarity metrics
  • Bias detection tools

Recommendation API & Frontend

Hill: 20%
•Must Haves (3)
  • REST API endpoints for recommendations
  • Student portal UI integration
  • Basic analytics tracking
~Nice to Haves (3)
  • GraphQL API support
  • Advanced recommendation explanations
  • Detailed click analytics

Daily Digest

Yesterday's Progress

  • • ML Recommendation Engine: 60% complete

Today's Focus

  • • Continue work on ML Recommendation Engine
  • • Continue work on Recommendation API & Frontend

Blockers

No blockers reported.

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Cool-down Management

Manage small tasks, bugs, and clean-up items for this cycle's cool-down period.

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