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non_personalized_recs.md

Introduction to Recommender Systems: Non-Personalized and Content-Based on Coursera

Information retrieval is the practice of asking questions about large documents.

  • It became especially popular when doing discovery for lawsuits
  • or AWS in guiding you to the relevant products
  • One of the first recommenders was GroupLens for newsnet

Collaborative Filtering: Involves running Ratings and Correlations through a CF engine.

  • The goal is to find a neighborhood of users
  • Recommendation Interfaces: Suggestion, top n
  • Prediction interfaces: Evaluate candidates and score/predictive rating

Ratings: can be implicit or explicit Preditions: Estimate of preferences around how much you like something

**Approaches to Recommendations: **

Content-Based Approaches:

https://www.w3.org/Conferences/WWW4/Papers/93/

  • Non-personalized/Stereotype (overall preference based on population)
  • Product Association: People who bought x also want x - also not personalized
  • Content-based: Based on metadata
  • Collaborative filtering: Learning preferences and using the community

Preferences and Ratings:

Preference: We want to learn what users like (can be broad) Rate / Review - Explicit Click/purchase/follow - Implicit

Explicit

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