1. Definition
“Information Retrieval (IR) is the activity of obtaining information resources relevant to an information need from a collection of information resources”
Information Overload

2. Why care about IR
We need to handle different information needs and different collections of resources
Information is Everywhere
New Information Created Every Minute

3. Recommendation Systems
Areas to focus
What we will learn this semester
Web Search
Query Suggestions
Question Answering
Recommendation Systems

4. Query Suggestions (QS)
Goal
Assist users by providing a list of suggested queries that could potentially capture their information needs

5. Query Suggestions (QS)
Existing methodologies
Query-log based
Given a user query Q, examine large amounts of past data to identify other queries that in users’ sessions that included Q
Corpus based (in the absence of query log)
Examine document corpus, e.g., Wikipedia, to determine the likelihood of (co-)occurrence of pairs of words/phrases
Regardless of the approach, QS modules
Apple
Apple pie
Apple TV CS
Food
Barcelona
Barcelona FC
Barcelona Spain
0.3
0.7
Offer diverse suggestions that multiple topical categories to which Q belongs or polysemy (terms with multiple meaning)
Need a ranking strategy to identify suggestions that most likely capture the intent of a user

6. Query Suggestions (QS)
Types of query refinement (reformulation)
Type
Goal
User Activity
Modification
Consider analogous, but not exactly-matching, terms
Q: “Single ladies song”
QS: “Single ladies lyrics”
Expansion
Generate a more “detailed” query that captures the real interest of a user
Q: “Sports Illustrated”
Q: “Sports Illustrated 2013”
Deletion
Create a more “high level”, i.e., less restrictive query
Q:“Ebay Auction”
QS: “Ebay”
Reformulation
Use the “original” query as a substring of a suggestion
Q:“pumpkin”
QS: “recipe for pumpkin cake”
Modification: people can write the same query in a number of different ways, but still looking for the same information
Expansion: try to get a “clearer” picture of what the user is looking for, i.e., yield a more “precise” query
Deletion: less restrictive query, try to get a more general overview of what the user might want

7. Query Suggestions (QS)
Challenges - Most of QS modules rely on query logs
Suitable for systems w/ large user base/interactions/past usage
Not suitable for small systems, e.g., desktop/personal search
Log-based QS modules cannot always infer “unseen” queries
(Long) Tail queries, i.e., rare queries
Difficult queries, i.e., queries referring to topics users are not familiar with

8. Web Search Why not just database (DB)?
Not all text resources will be properly structured
Text resources versus DB records
(DB) Matches are easily found by comparisons
“Find accounts with balance > $100”
(IR) Unstructured text require non-trivial comparisons
“Information Retrieval Conferences”

9. Web Search IR goal of web search Main IR issues related to web search
Retrieve all the docs that are relevant to a query, while retrieving as few non-relevant documents as possible
Main IR issues related to web search
Comparing a query to text resources and determining what is a good match
A relevant document/resource contains the information a user U was looking for when U submits a query to the search engine
Many factors influence a person’s decision about what is relevant: e.g., task, context, novelty, background

10. Web Search
Retrieval models, based on which ranking algorithms used in search engines are developed, define a view of relevance
Most models describe statistical (rather than linguistic) properties of text, e.g., counting simple text features, such as word occurrences, instead of parsing/analyzing sentences
More information can be considered for relevance ranking, e.g., document topics, user context, etc.
Remember: exact matching of words is not enough
There are many different ways to express the same thing in languages like English

11. Web search – Design Issues
Performance
Measuring/improving the efficiency of search, e.g., reducing response time, increasing query throughput, increasing indexing (data structures designed to improve search) speed
Dynamic data
The “collection” for most real applications is constantly changing in terms of updates, additions & deletions
Acquiring or “crawling” the documents is a major task
Typical measures are coverage (how much has been indexed) and freshness (how recently was it indexed)
Updating the “indexes” while processing queries is also a design issue

12. Web search – Design Issues
Scalability
Making everything work with millions of users every day, and many terabytes of documents
Distributed processing is essential
Adaptability
Changing and tuning search engine components, such as ranking algorithm, indexing strategy, interface for different applications

13. Question Answering (QA)
Goal
Automatically answer questions submitted by humans in a natural language form
Approaches
From diverse areas of study, such as IR, NLP, Ontologies, and ML, to identify users’ information needs & textual phrases as potentially suitable answers for users
Exploit
IR: content analysis/matching
NLP: semantic and syntactic analysis of content
Ontologies: concepts and relationships between concepts for entity/name recognition
Machine learning: analyze patterns to identify user’s needs or formats of expected answer
Data from Community Question Answering Systems (CQA)
(Web) Data Sources, i.e., doc corpus

14. Question Answering (QA)
CQA-based
CQA-based approaches
Analyze questions & corresponding answers archived at CQA sites to locate answers to a new question
Exploit “wealth-of-knowledge” already provided by CQA users
Community Question Answering System
“CQA systems exploit the power of human knowledge to satisfy a broad range of users’ information needs”
Existing popular CQA sites
Yahoo! Answers, WikiAnswers, and StackOverflow

15. Question Answering
CQA-based
Example

16. Question Answering (QA)
CQA-based
Challenges for finding an answer to a new question from QA pairs archived at CQA sites
Incorrect Answers
Misleading Answers
No Answers
Answerer reputation
Spam Answers
SPAM

17. Question Answering (QA)
CQA-based
Challenges (cont.)
Account for the fact that questions referring to the same topic might be formulated using similar, but not the same, words
Identifying the most suitable answer among the many available

18. Question Answering (QA)
Corpus-based
Corpus-based approaches
Analyze text documents from diverse online sources to locate answers that satisfy the information needs expressed in a question
“When is the next train to Glasgow?”
Question
Text Corpora & RDBMS
Data sources QA
SYSTEM
“8:35, Track 9.”
Answer
Question
Extract Keywords
Query
Search Engine
Corpus
Docs
Passage Extractor
Answers
Answer Selector
Answer

19. Recommendation Systems (RS)
Goal
Enhance users’ experience by assisting them in finding information (due to the information overload problem) & reducing search and navigation time
User Profile & Contextual Parameters
Community Data
Knowledge Models
Title
Author
Genre …
Importance
RS are software agents that elicit the interests and preferences of individual consumers […] and make recommendations accordingly. They have the potential to support and improve the quality of the decisions consumers make while searching for and selecting products online. (Xiao & Benbasat 2007)
Product/Service Features RS
Predictive
Top-N

20. Recommendation Systems (RS)
Examples
IMDB.com
Amazon.com
LibraryThing.com

21. Recommendation Systems (RS)
Approaches
Machine Learning
Rating?
Movie
Genre
Actor
Ontology-based
Degree of
Overlap?
Information Retrieval
Degree of
Similarity?
Despicable Me is a surprisingly thoughtful, family-friendly treat with a few surprises of its own.
When a criminal mastermind uses a trio of orphan girls as pawns for a grand scheme… 
Appealing kid-friendly comedy; some scary scenes.
Po and his friends fight to stop a peacock villain from conquering China… 

22. Recommendation Systems (RS)
Categorization
Content-based: examine textual descriptions of items
Collaborative-filtering: examine historical data in the form of user and item ratings
Hybrid: examine content, ratings, and other features to make suggestions
Other considerations
Target of recommendations, e.g., books suggested for an individual vs. groups of people
Purpose of recommendations, e.g., family vs. friends
Trust-based recommendation, e.g., considering the opinion/suggestions of the social network of a user

23. Recommendation Systems (RS)
Challenges
Capture users’ preferences/interests
What type of information should be included in users’ profiles & how is this information collected?
Finding the relevant data for describing items
What metadata should be considered to best capture an item?
Introduce “novelty” and “serendipity” to recommendations
Provide variety among suggestions, e.g., suggesting “Kung-Fu Panda 2” to someone who has viewed “Kung-Fu Panda” is not unexpected

24. Recommendation Systems (RS)
Challenges (continued)
Personalization: Avoid “one-size-fits-all”, like Amazon’s recommender that provides to every user the same suggestion
Cold start: no information on new items/users
Sparsity: very few items are assigned a high number of ratings
Popularity bias: well-known items are favored at the time of providing ratings
Remember! Items other than products can be recommended as well: friends, activities, groups…