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Questions and Answers

What is a common issue with user-based evaluations of movie reviews?

Users often assign scores that are systematically higher. (correct)

Users have no prior experience with films.

Users rate movies based on technical aspects only.

Users tend to watch only popular films.

What is necessary to ensure unbiased user-based evaluations?

Select movies based on release dates.

Incorporate users' opinions before testing.

Limit user feedback to a specific demographic.

Employ statistical models to analyze user inputs. (correct)

In evaluating search systems, which is a key factor in determining effectiveness?

The type of search words used.

The time it takes to configure the search.

The relevance and order of returned documents. (correct)

The number of search engines available.

What does the recall metric specifically measure in the evaluation process?

The percentage of relevant documents that were retrieved. Signup and view all the answers

Which method is deemed safer for analyzing performance data?

Carrying out blind analyses after data collection. Signup and view all the answers

What should be done before constructing a method to evaluate a search system?

Define the desired answer for evaluation questions. Signup and view all the answers

What is a disadvantage of basing performance evaluations solely on user feedback?

Users can provide inconsistent results. Signup and view all the answers

What does the term 'model-based evaluation' involve?

Comparing results against a theoretical framework. Signup and view all the answers

What does the mean average precision (MAP) primarily indicate?

A single value representing the performance of a ranking system. Signup and view all the answers

How is the mean average precision (MAP) calculated?

By averaging the precision at various ranks for multiple queries. Signup and view all the answers

In the context of mean average precision, why is rank 1 considered twice as important as rank 2?

Because it indicates immediate relevance. Signup and view all the answers

What does mean reciprocal rank (MRR) measure?

The reciprocal of the rank for the first relevant document. Signup and view all the answers

What is a key feature of the CISI collection used in information retrieval evaluation?

Contains 1430 documents and 112 queries Signup and view all the answers

Under which condition is mean reciprocal rank (MRR) specifically defined?

When there is exactly one relevant answer. Signup and view all the answers

What is the effect of assigning more weight to lower ranks in MAP calculations?

It negatively impacts the overall MAP score. Signup and view all the answers

Which statement accurately describes the TREC evaluations?

TREC provides evaluation tools for interacting with test data sets Signup and view all the answers

What does pooling refer to in the context of large document collections?

Assessing a random sample of the document collection Signup and view all the answers

Which measure is preferred when assessing systems with multiple relevant answers?

Mean average precision (MAP). Signup and view all the answers

What is a characteristic of relevance in information retrieval judging?

Relevance can change based on the time of assessment Signup and view all the answers

What does a higher mean reciprocal rank (MRR) indicate?

Better overall performance of the retrieval system. Signup and view all the answers

What type of documents does the Wall Street Journal collection predominantly contain?

Full-text newspaper articles Signup and view all the answers

What was the primary focus of earlier works in information retrieval evaluations?

Automated library systems using Cranfield tests Signup and view all the answers

What is typically included in a test collection for information retrieval?

A mix of queries and documents with relevance judgements Signup and view all the answers

What is the average number of relevant documents per query found in the Wall Street Journal collection?

30 Signup and view all the answers

What does precision measure in the context of information retrieval?

The proportion of relevant documents retrieved among all retrieved documents Signup and view all the answers

How is recall defined in information retrieval?

The ratio of relevant documents retrieved to the total relevant documents available Signup and view all the answers

What is the significance of the sawtooth shape in precision vs recall graphs?

It represents the performance inconsistency of search systems for different queries Signup and view all the answers

Which method is used for optimistic interpolation in precision-recall curves?

Using the maximum precision at or to the right of the recall point Signup and view all the answers

What is Average Precision (AP) used to represent?

The average precision across multiple recall thresholds for evaluated queries Signup and view all the answers

In the context of precision calculations, what does P @ threshold represent?

Precision evaluated at a specific rank of document retrieval Signup and view all the answers

Why is it necessary to interpolate precision-recall curves?

To ensure a smooth distribution and form an average from multiple queries Signup and view all the answers

How is the sum of precision values represented mathematically in the Average Precision formula?

$AP = \sum_{i=1}^{n} p_{\tau_i}$ Signup and view all the answers

What is the purpose of using several information retrieval systems in the pooling process?

To maximize the chances of finding relevant documents Signup and view all the answers

What is a drawback of initial pooling in document retrieval?

It may miss some relevant documents Signup and view all the answers

What are test collections primarily used for?

To conduct repeatable experiments and compare system results Signup and view all the answers

What is the main advantage of K-fold cross-validation in training recommender systems?

It provides an unbiased estimate of model performance Signup and view all the answers

Which statement correctly describes N-1 testing?

It selects one value to withhold for testing during each iteration Signup and view all the answers

What characterizes the web as a test collection?

It contains several billion dynamic web pages Signup and view all the answers

Which dataset is known for containing jokes and their ratings?

Jester Signup and view all the answers

What is the main purpose of pooling in information retrieval?

To compile a diverse set of documents for relevance evaluation Signup and view all the answers

What does a False Positive (FP) represent in a confusion matrix?

Predicted Yes, but the true value is No Signup and view all the answers

Which metric is frequently used as an alternative name for Recall?

True Positive Rate Signup and view all the answers

How is Accuracy calculated in a confusion matrix?

TP + TN / (TP + FP + TN + FN) Signup and view all the answers

What does the F1 Score represent in terms of model performance?

The harmonic mean of Precision and Recall Signup and view all the answers

Which type of error is associated with False Negatives (FN)?

Type 2 error Signup and view all the answers

What is the primary use of the Mean Absolute Error (MAE)?

To evaluate the average of absolute differences between predicted and actual values Signup and view all the answers

What does A/B Testing primarily evaluate?

The performance difference between two systems Signup and view all the answers

Which condition makes Precision tend to 1 in a confusion matrix?

False Positives decrease Signup and view all the answers

Study Notes

Evaluation Methods

Evaluation involves comparing performance against a benchmark, whether it's a mathematical model, user expectations, or some other criterion.
Deciding what constitutes "good" performance is a key initial step.
Performance can be evaluated through comparison with a mathematical model, or the expectations of users.

User-Based Evaluation

User biases can affect evaluation results. Analyst expectations can cause misinterpretations of feature performance.
Even metrics for thought processes can't completely eliminate bias.
User input is sometimes needed for high-level tests, with a statistical model needed to underpin these tests.
Blind analyses (where tests are designed, run, and assessed independently) are preferable
Design tests using background or simulation data.
Avoid adjusting analyses when looking at the final dataset.
Movie reviews often exhibit bias. Users tend to rate movies they like higher than average. This bias needs correction.
Search engine users have different expectations. Different types of search results are needed. For example, some users may want academic articles, while others may want magazine articles.
PageRank and quality aren't enough to evaluate search engines effectively.
The addition of recommendations systems can be needed to refine search engine results.

Model-Based Evaluation

A measurement is compared against a model.
A model provides a central value and an uncertainty band.
To proceed, an observable is chosen, and the expected value is determined.

Evaluation of Search Systems

Search system performance needs to be quantified. Key questions to be asked include;
Is the correct information being retrieved?
Is the information being returned in the correct order?
Are results being returned in a timely manner?
Are results displayed appropriately?
Evaluation methods need to be defined to answer these questions.
Potential uncertainties of the evaluation method must be taken into account.

Recall

Recall measures the proportion of relevant documents retrieved from the total set of relevant documents.
A higher recall value means a larger proportion of relevant documents were retrieved.
Formula: Recall = (Relevant retrieved documents ∩ Retrieved documents)/Total relevant documents

Precision

Precision measures the proportion of retrieved documents that are relevant to the total number of retrieved documents.
A higher precision value implies that a higher fraction of retrieved documents are relevant.
Formula: Precision = (Relevant retrieved documents ∩ Retrieved documents)/Total Retrieved documents

Recall and Precision - Example Data

Example data from various queries shows how recall and precision can vary. Data suggests that precision is typically higher for the first documents retrieved in a search.

Evaluation of Partial Sets

Rank order can be used to evaluate queries.
Properties of earlier results are often more important.
Precision@k measures the proportion of top-k results that are relevant.
Recall@k measures the proportion of relevant documents that are among the top-k results.

Average Precision (AP) and Mean Average Precision (MAP)

Average Precision (AP) is the average precision for a given threshold or rank of documents in a search query.
Mean Average Precision (MAP) is the average of the average precision for multiple queries.
Individual words retrieved can be weighted to be more impactful that those retrieved later in the order.
The precision is typically higher for the first rank

Other Evaluation Metrics

Other metrics include "Bpref"; (relevant documents ranked higher than irrelevant documents);
Relative recall and
Geometric Mean Average Precision (GMAP).
Statistical tests can determine if one system is better than another.

Test Data

Test data needs to include tagged relevance.
Individual queries need to be tagged as relevant.
Average performance for each system can then be calculated.

Information Retrieval Evaluation

Early evaluation primarily used Cranfield tests on library systems.
More recent studies use simulations built from Text REtrieval Conference (TREC) data.

System Evaluation: Test Collection

A wide range of document types (text, images, videos, speech, etc), are selected.
Collection creators often use user-provided information requests

CISI and WSJ Collections

The CISI collection contains 1,430 documents and 112 queries, with an average of 41 relevant documents per query.
The WSJ collection contains 74,520 documents (from Wall Street Journal newspaper articles) and 50 topics; approximately.

TREC

TREC provides many different datasets and tools for interface with test data.

Pooling

Pooling is a technique used to evaluate large collections when manual assessment is needed.
Random sampling from an assessed document pool is used
Often better to include several systems to initially generate the pool

Web as Test Collection

Evaluating web search involves a massive amount of data (billions of pages) and short, dynamic query terms. Creating a snapshot of this data is required, then the pooling method can be applied.

Evaluation of Recommender Systems

Static data with user ratings is used

Training and Testing

Data is split into training and testing sets. Training sets are used to train the recommender system, and testing sets are used to evaluate the trained system

K-Fold Cross-Validation

Bias is reduced by repeating training and testing from different sections of the dataset.

N-1 Testing

This method evaluates a recommender system for a single active user, where data for one user is deliberately withheld from training.

Confusion Matrix

A confusion matrix is used to evaluate the effectiveness of a classification system. It shows outcomes and predictions of an evaluation.
Common confusion matrix measures include precision; recall; accuracy and; F1 (harmonic mean).

Prediction Accuracy

Error values are defined to measure accuracy. This data is used to train machine learning algorithms.
Error formulae include Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE).

Significance Testing

Statistical testing compares the effectiveness of systems (A vs B).
The effect, if statistically significant, can be evaluated with likelihood functions, and systematic errors taken into account.

A/B Testing

A/B testing is a controlled experiment with two options or versions that diverts users to test an environment.
Used to evaluate the impact of new features, or compare differences.

Other Evaluation Approaches

Prototypical users can be involved in test tasks to assess user satisfaction.
Previous system usage can also be reviewed with an analysis of accumulated logs to improve overall understanding of system performance.

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