introduction

building blocks of search engines
- search (user initiates)
- reccomendations - proactive search engine (program initiates e.g. pandora, netflix)
- information retrieval - activity of obtaining info relevant to an information need from a collection of resources
- information overload - too much information to process
- memex - device which stores records so it can be consulted with exceeding speed and flexibility (search engine)
IR pieces
1. Indexed corpus (static)
  - crawler and indexer - gathers the info constantly, takes the whole internet as input and outputs some representation of the document
    - web crawler - automatic program that systematically browses web
  - document analyzer - knows which section has what -takes in the metadata and outputs the index (condensed), manage content to provide efficient access of web documents
2. User
  - query parser - parses the search terms into managed system representation
3. Ranking
  - ranking model -takes in the query representation and the indices, sorts according to relevance, outputs the results
  - also need nice display
  - query logs - record user’s search history
  - user modeling - assess user’s satisfaction
steps
1. repository -> document representation
2. query -> query representation
3. ranking is performed between the 2 representations and given to the user
4. evaluation - by users
information retrieval:
1. reccomendation
2. question answering
3. text mining
4. online advertisement

they are all getting closer, database approximate search and information extraction converts unstructed data to structured:

database systems	information retrieval
structured data	unstructured data
semantics are well-defined	semantics are subjective
structured query languages (ex. SQL)	simple keyword queries
exact retrieval	relevance-drive retrieval
emphasis on efficiency	emphasis on effectiveness

natural language processing - currently the bottleneck
- deep understainding of language
- cognitive approaches vs. statistical
- small scale problems vs. large
developing areas
- currently mobile search is big - needs to use less data, everything needs to be more summarized
- interactive retrieval - like a human being, should collaborate
core concepts
- information need - desire to locate and obtain info to satisfy a need
- query - a designed representation of user’s need
- document - representation of info that could satisfy need
- relevance - relatedness between documents and need, this is vague
  - multiple perspectives: topical, semantic, temporal, spatial (ex. gas stations shouldn’t be behind you)
Yahoo used to have system where you browsed based on structure (browsing), but didn’t have queries (querying)
- better when user doesn’t know keywords, just wants to explore
- push mode - systems push relevant info to users without a query
- pull mode - users pull out info using keywords

related fields