1. Introduction (10 mins)

• Welcome and Overview

• Introduction to the workshop: What are recommendation systems and why do they matter?

• Real-world examples:

  • Dating apps (e.g., Tinder)
  • Content feeds (e.g., Instagram)
  • Food recommendations (e.g., Zomato)

  • Goals of the workshop:

  • Learn how RecSys work

  • Dive into their challenges
  • Understand their ethical implications

• Audience Poll / Engagement

• Quick questions to gauge familiarity with RecSys

• Brief survey:

  • Who uses these systems?
  • Who’s familiar with the tech behind them?

2. Foundations of Recommendation Systems (30 mins)

Goal: Give a strong conceptual grounding before diving into implementation.

• Types of Training Data

• Explicit Feedback: Ratings, thumbs-up/down

• Implicit Feedback: Clicks, dwell time, views, purchases

• Interaction Types

• Positive-only vs. Positive & Negative Feedback

• System Categories by Use-Case

• General-purpose, Context-aware, Sequential, Knowledge-based RecSys

• Core Approaches

• Collaborative Filtering (User-based vs. Item-based)

• Content-Based Filtering

• Hybrid Models

• Application Examples

• Netflix → Hybrid

• Amazon → Item-based CF
• Tinder → Context-aware
• Spotify → Sequential
• Zomato → Content + CF

• Pratilipi → Long-tail engagement via diversity-aware models

• Recommendation Pipeline

• Retrieval Stage: Candidate generation

• Ranking Stage: Sorting by relevance

• Exploration vs. Exploitation

• Ranking Methods

• Pointwise / Pairwise / Listwise

• Metrics that Matter

• Offline Metrics: Precision\@K, Recall, MAP, NDCG

• Business Goal Alignment:

  • YouTube → Watch Time
  • Tinder → Match Rate

3. Hands-On Session: Building a Recommendation System (40 mins)

Goal: Implement and understand the inner workings of a real model.

• Exercise 1: Building a Two-Tower Model (30 mins)

• Use PyTorch to build a simple two-tower architecture

• Demonstrate user-item embedding and dot-product similarity

• Step-by-step: Data loading → Model → Loss → Training

• Exercise 2: Evaluating Your Model (10 mins)

• Use standard metrics: Precision, Recall, MAP

• Compare online metrics vs. offline metrics

Break (10 mins)

4. Challenges in Recommendation Systems (30 mins)

Real-World Problems

• Bias in Training Data: Echo chambers, skewed preferences
• Cold Start: New users/items with little interaction data
• Scalability: Latency and compute at scale

Ethical Challenges

• Algorithmic Bias & Fairness: Impact on sensitive attributes
• Diversity vs. Personalization: Avoiding homogenized content

• Debiasing Techniques:

• Exposure Bias: Overexposure to certain items

• Popularity Bias: Trend dominance
• Selection Bias: Skewed relevance due to interaction patterns

Case Study: Pratilipi

• How an Indian storytelling platform balances fairness, diversity, and engagement

5. Hands-On Recap & Final Exercise (30 mins)

Mini Lab: Use your model to explore bias, diversity, and fairness.

• Apply:

• Diversity boosting

• Popularity debiasing

• Freshness filters

• Re-evaluate:

• Compare fairness, diversity, and relevance metrics before and after

• Group Reflection:

• Discuss improvements, regressions, and trade-offs

6. Gaming the System: How Users Can Personalize Their Feeds (10 mins)

• How users can influence their recommendation feed (e.g., YouTube, Spotify)
• Implications of over-personalization (filter bubbles, doomscrolling)
• Tips for better and diverse recommendations

7. Conclusion & Q\&A (20 mins)

• Wrap-Up
• Q\&A: Open floor for questions on theory, implementation, or ethics

• Additional Resources:

• GitHub repo with code, datasets, and readings

• Encourage continued learning and experimentation