C1

Question 1

information retrieval

Answer 1

finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections

Question 2

how do IR systems support the search process?

Answer 2

• analyzing queries and documents
• retrieving documents by computing a relevance score for each document given a query
• ranking the documents by that relevance score (true relevance is typically binary)

Question 3

how does a search engine work? (basic)

Answer 3

• user has an information need
• user types a query
• system returns item sorted by relevance to that query, based on match on the query and popularity

Question 4

how do recommender systems work? (basic)

Answer 4

• user has an interest
• user goes to a service/app/website
• system provides items that are relevant to the user based on user history/profile and popularity

Question 5

4 principles of IR

Answer 5

principles of
- relevance
- ranking
- text processing
- user interaction

Question 6

principles of relevance

Answer 6

• term overlap: return documents that contain the query terms (or related terms?)
• document importance (PageRank)
• result popularity: clicks by previous users on the document
• diversification: different types of results for different interpretations of the query

Question 7

evaluation of relevance

Answer 7

needed:
- a set of queries
- a document collection
- relevance assessment: set of documents for each query that are labelled as relevant or non-relevant

the retrieval system returns a ranking of all documents

Question 8

principles of ranking

Answer 8

• estimate relevance for each document
• we need a score
• term weighting (basic notion)
• PageRank

Question 9

machine learning for ranking

Answer 9

Idea: learn the relevance of a document based on human-labelled training data (relevance assessments)

Question 10

Why is machine learning for ranking different from machine learning for classification?

Answer 10

Relevance depends on the query, so we cannot train a global classifier over all relevant and irrelevant documents in a labelled dataset
=> need a machine learning paradigm that includes the query

Question 11

two-stage retrieval first stage

Answer 11

• from large collection
• unsupervised
• often term-based (sparse)
• priority: recall

Question 12

two stage retrieval second stage

Answer 12

• ranking top-n documents from first stage
• supervised
• often based on embeddings (dense)
• priority: precision at high ranks

Question 13

2 different relavance models for similarity

Answer 13

• term overlap: find documents that contain query words
• semantic similarity: find documents which semantic representation is close to the query

Question 14

index time

Answer 14

• collect (new) documents
• pre-process the documents
• create the documents representation
• store the documents in the index
• indexing can take time

Question 15

query time

Answer 15

• process the user query
• match the query to the index
• retrieve the documents that are potentially relevant
• rank the documents by relevance score
• retrieval cannot take time (< 1 sec)

Question 16

term-based retrieval models

Answer 16

• split documents and queries into terms
• normalize terms to a common form (lowercase, remove diacritics and stop words, stemming/lemmatization, map similar terms together)
• most used model is BM25

Question 17

problem with term-based retrieval

Answer 17

a vocabulary mismatch between query and document
solution: semantic matching, based on embeddings representations of texts

Question 18

the role of users in IR

Answer 18

• user has an information need
• user defines what is relevant
• user interacts with search engine

Question 19

challenges with user interaction

Answer 19

• real user is hidden
• user queries are short and ambiguous (what does the query refer to? what should be the mode of the answer? what type of information is the user interested in?)
• natural text is unstructured, noisy, redundant, infinite, sparse and multilingual