1. Intro to Recommender Systems

<aside> 💡 Recommender systems capture the pattern of people’s behavior and use it to predict what else they might want or like

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Advantages of recommender systems

• Broader exposure
• Possibility of continual usage purchase of products
• Provide netter experience

Two types of recommender systems

Content-Based

‘Show me more of the same of what I’ve liked before‘

Collaborative Filtering

‘Tell me what’s popular among my neighbors, I also might like it.'

Implementing recommender systems

• Memory-based

  • Uses the entire user-item dataset to generate a recommendation

  • Uses statistical techniques to approximate user or items

    E.g.Pearson Correlation, Cosine Similarity, Euclidean Distrance, etc

• Model-based

  • Develops a model of users in an attempt to. learn their preferences
  • Models can be created using Machine Learning techniques like regression, clustering, classification, etc.

2. Content-based Recommender Systems

3. Collaborative Filtering

• User-based collaborative filtering
  • Based on user’s neighborhood
• Item-based collaborative filtering
  • Based on items’ similarity

Challenges of collaborative filtering

• Data Sparsity
  • User in general rate only a limited number of items
• Cold Start
  • Difficulty in recommendation to new users or new items
• Scalability
  • Increase in number of users or items