Recommender System: Basic Concepts

5th July 2017 at 10:07am
Recommender System

History

  • Ants, Cavemen, and Early Recommender Systems – The emergence of critics
  • Information Retrieval and Filtering
  • Manual Collaborative Filtering
  • Automated Collaborative Filtering
  • The Commercial Era

Information Retrieval

  • Concept: An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevancy.
  • Static content base – Invest time in indexing content
  • Dynamic information need – Queries presented in “real time”
  • Common approach: TFIDF (tf-idf, term frequency–inverse document frequency)
    • Rank documents by term overlap
    • Rank terms by frequency

Information Filtering

  • Concept: a approach that select relevant information for a user based on the user's profile
  • Characteristic:
    • Reverse assumptions from Information Retrieval:
      • Static information need
      • Dynamic content base
    • Invest effort in modeling user need
      • Hand‐created “profile”
      • Machine learned profile
      • Feedback/updates
    • Pass new content through filters

Collaborative Filtering

In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating).

The most common application of collaborative filtering is user-based k-nearest neighbors algorithm.

Preference

Explicit preference:

  • Rating
  • Review
  • Vote

Implicit preference:

  • Click
  • Purchase
  • Follow

Ratings: star, thumbs / like, etc.

Predictions and Recommendations

  • Predictions: Estimates of how much you’ll like an item
    • Often scaled to match some rating scale
    • Often tied to search or browsing for specific products
  • Recommendations: suggestions for items you might like (or might fit what you’re doing)
    • Often presented in the form of “top-n lists”
    • Also sometimes just placed in front of you