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Evaluation measures (information retrieval)

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Background and Importance of Evaluation Measures in Information Retrieval
– Indexing and classification methods have a long history in information retrieval.
– Evaluation measures for IR systems began in the 1950s with the Cranfield paradigm.
– The Cranfield tests established the use of test collections, queries, and relevant items for evaluation.
– Cleverdon’s approach influenced the Text Retrieval Conference series.
– Evaluation measures are crucial for search engines, databases, and library catalogues.
– Evaluation of IR systems is essential for internet search, website search, and library catalogues.
– Evaluation measures are used in information behavior studies and usability testing.
– Academic conferences like TREC, CLEF, and NTCIR focus on evaluation measures.
– IR research relies on test collections and evaluation measures to measure system effectiveness.
– Evaluation measures help assess business costs and efficiency.
– Evaluation measures in information retrieval are used to assess the effectiveness and performance of search algorithms and systems.
– These measures play a crucial role in determining the quality of search results and the overall user experience.
– They provide a standardized way to compare different retrieval systems and algorithms.
– Evaluation measures help researchers and practitioners in making informed decisions regarding the design and improvement of information retrieval systems.

Online and Offline Evaluation Metrics
– Online metrics are derived from search logs.
– Online metrics are used to determine the success of A/B tests.
– Session abandonment rate measures the ratio of search sessions without clicks.
– Click-through rate (CTR) measures the ratio of users who click on a specific link.
– Session success rate measures the ratio of user sessions that lead to a successful result.
– Offline metrics are based on relevance judgment sessions.
– Judges score the quality of search results using binary or multi-level scales.
– Precision measures the fraction of retrieved documents that are relevant.
– Recall measures the fraction of relevant documents successfully retrieved.
– Fall-out measures the proportion of non-relevant documents retrieved.

F-score / F-measure
– The F-score is the weighted harmonic mean of precision and recall.
– It provides a balanced evaluation of precision and recall.
– The F-score is commonly used in information retrieval evaluation.
– It helps assess the overall effectiveness of an IR system.
– The F-score considers both precision and recall in its calculation.
– F-measure is calculated using precision and recall.
– It is also known as the F1 measure.
– The general formula for F-measure is (2 * precision * recall) / (precision + recall).
– Other commonly used F-measures include F2 measure and F0.5 measure.
– F-measure combines information from both precision and recall to represent overall performance.

Precision at k and R-precision
– Recall is no longer a meaningful metric in modern information retrieval.
– Precision at k (P@k) is a useful metric that considers the top k retrieved documents.
– P@k fails to account for the positions of relevant documents among the top k.
– Scoring manually is easier for P@k as only the top k results need to be examined.
– Even a perfect system will have a score less than 1 on queries with fewer relevant results than k.
– R-precision requires knowing all relevant documents for a query.
– The number of relevant documents (R) is used as the cutoff for calculation.
– R-precision is equivalent to precision at the R-th position and recall at the R-th position.
– It is often highly correlated to mean average precision.
– R-precision is calculated as the fraction of relevant documents among the top R retrieved documents.

Mean Average Precision (MAP) and Discounted Cumulative Gain (DCG)
– MAP is the mean of the average precision scores for a set of queries.
– It provides an overall measure of performance.
– MAP is calculated by summing the average precision scores for each query and dividing by the number of queries.
– It is commonly used in information retrieval evaluation.
– MAP takes into account the precision at different recall levels.
– DCG evaluates the usefulness of documents based on their position in the result list.
– It uses a graded relevance scale and penalizes lower-ranked relevant documents.
– DCG is calculated as the sum of relevance values logarithmically proportional to the position.
– Normalized DCG (nDCG) compares performances using an ideal DCG.
– nDCG values can be averaged to measure the average performance of a ranking algorithm.

Evaluation measures for an information retrieval (IR) system assess how well an index, search engine or database returns results from a collection of resources that satisfy a user's query. They are therefore fundamental to the success of information systems and digital platforms. The success of an IR system may be judged by a range of criteria including relevance, speed, user satisfaction, usability, efficiency and reliability. However, the most important factor in determining a system's effectiveness for users is the overall relevance of results retrieved in response to a query. Evaluation measures may be categorised in various ways including offline or online, user-based or system-based and include methods such as observed user behaviour, test collections, precision and recall, and scores from prepared benchmark test sets.

Evaluation for an information retrieval system should also include a validation of the measures used, i.e. an assessment of how well they measure what they are intended to measure and how well the system fits its intended use case. Measures are generally used in two settings: online experimentation, which assesses users' interactions with the search system, and offline evaluation, which measures the effectiveness of an information retrieval system on a static offline collection.

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