Evaluation measures (information retrieval)

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.