This note mainly focuses on text information retrieval. It is mainly based on JHU's, ETHzurich's and Stanford's information retrieval course.
Data -> Search -> User
Data: Getting documents and preprocessing documents
Text preprocessing, Clustering, Information Extraction (Named Entity, Relation, Topic Models, etc.)
Forward Index, Inverted Index
Search: Querying content (Search engine) or filtering content (Recommendation system). Not different that much.
Querying: Boolean Retrieval, Vector Space Model, Probabilistic Model, Learning to Rank
Filtering: Content Filtering, Collaborative Filtering, Also use Querying methods
Ranking: Scoring, Link Analysis
User: Content presentation
Retrieval In General
The way user accessing data: Push mode (Recommendation system like news feed) -> filtering content and Pull model (Search engine) -> querying content.
Retrieval compared to Database: Database usually holds structured data, with well-defined query semantics.
Know that user information need is almost always larger than the given query.
Search core methods: Selection (binary decision) or Ranking (Continous scoring and thresholding). If we assume the utility of a document to a user is independent of any other document and the usesr browse the results sequentially, we could rank documents in descending order of the probability that a document is relevant to the query.