- Ants, Cavemen, and Early Recommender Systems – The emergence of critics
- Information Retrieval and Filtering
- Manual Collaborative Filtering
- Automated Collaborative Filtering
- The Commercial Era
- 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
- Concept: a approach that select relevant information for a user based on the user's profile
- Reverse assumptions from Information Retrieval:
- Static information need
- Dynamic content base
- Invest effort in modeling user need
- Hand‐created “profile”
- Machine learned profile
- Pass new content through filters
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.
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