MRR
An information retrieval evaluation metric that measures the position of the first correct answer in a ranked list of results returned by a retrieval system. The higher the rank of the first correct answer, the higher the resulting score.
Core Facts
Timeline (last 90 days)
Evaluation of embedding model fine-tuning looks not at loss but at retrieval metrics such as Recall@K and MRR.
Common metrics for recall quality evaluation include Recall@K, MRR (mean reciprocal rank), and hit rate.
MRR (Monthly Recurring Revenue) is defined as the sum of all active subscriptions' recurring revenue at a point in time converted to a monthly basis, with one-time fees excluded and annual/quarterly subscriptions prorated.
Stripe, as a payment gateway, records the flow of funds, while subscription analytics tools such as ChartMogul record changes in subscription relationships; the two handle the same transaction with different conventions, causing MRR discrepancies.
For evaluation metrics, retrieval-layer metrics (such as Recall@k and MRR, based on canonical IDs) can be reported separately from generation-layer metrics (fluency and language naturalness).
The gate uses only recall@5 as a hard threshold, with MRR and nDCG@5 as observational metrics, to avoid ranking fluctuations causing unstable builds.
Recall measures the proportion of all relevant documents that are actually retrieved by the system, while ranking quality concerns the position distribution of relevant documents in the result list, with commonly used metrics including MRR and NDCG.
Success criteria are quantified thresholds agreed upon in advance, such as recall, MRR, or NDCG reaching a certain threshold.
The EdgeWise selector is evaluated using ranking metrics such as MRR (Mean Reciprocal Rank); when MRR approaches 1, it can precisely route key steps to the cloud.
In the past few years, a clear split has emerged within the indie hacking community: one group continues to pursue MRR (monthly recurring revenue) and growth metrics, while another group has turned to the "small web" or "personal software" movement, treating projects as vehicles for expression and self-regulation rather than as a springboard for entrepreneurship.
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All Facts (20)
MRR (Mean Reciprocal Rank) is more sensitive to top-ranked results, with a correct tool ranked 1st scoring 1 point, 2nd scoring 0.5 points, and 5th scoring 0.2 points.
75%UnverifiedARR (Annual Recurring Revenue) is calculated by multiplying the Monthly Recurring Revenue (MRR) at a given point in time by 12.
60%UnverifiedARR is calculated by multiplying the monthly recurring revenue (MRR) at a given point in time by 12.
60%UnverifiedEvaluation of embedding model fine-tuning looks not at loss but at retrieval metrics such as Recall@K and MRR.
50%UnverifiedCommon metrics for recall quality evaluation include Recall@K, MRR (mean reciprocal rank), and hit rate.
50%UnverifiedMRR (Monthly Recurring Revenue) is defined as the sum of all active subscriptions' recurring revenue at a point in time converted to a monthly basis, with one-time fees excluded and annual/quarterly subscriptions prorated.
50%UnverifiedStripe, as a payment gateway, records the flow of funds, while subscription analytics tools such as ChartMogul record changes in subscription relationships; the two handle the same transaction with different conventions, causing MRR discrepancies.
50%UnverifiedFor evaluation metrics, retrieval-layer metrics (such as Recall@k and MRR, based on canonical IDs) can be reported separately from generation-layer metrics (fluency and language naturalness).
50%UnverifiedThe gate uses only recall@5 as a hard threshold, with MRR and nDCG@5 as observational metrics, to avoid ranking fluctuations causing unstable builds.
50%UnverifiedRecall measures the proportion of all relevant documents that are actually retrieved by the system, while ranking quality concerns the position distribution of relevant documents in the result list, with commonly used metrics including MRR and NDCG.
50%UnverifiedSuccess criteria are quantified thresholds agreed upon in advance, such as recall, MRR, or NDCG reaching a certain threshold.
50%UnverifiedThe EdgeWise selector is evaluated using ranking metrics such as MRR (Mean Reciprocal Rank); when MRR approaches 1, it can precisely route key steps to the cloud.
50%UnverifiedIn the past few years, a clear split has emerged within the indie hacking community: one group continues to pursue MRR (monthly recurring revenue) and growth metrics, while another group has turned to the "small web" or "personal software" movement, treating projects as vehicles for expression and self-regulation rather than as a springboard for entrepreneurship.
50%UnverifiedMRR is defined as: the reciprocal of the rank of the first expected text chunk, or 0 if it is not found.
50%UnverifiedMRR (Monthly Recurring Revenue) is a core metric in the SaaS industry, referring to the total subscription revenue a product can expect to continue generating from all paying users in a given month; investors usually estimate a SaaS company's valuation as a multiple of MRR, with common multiples for early-stage projects being 3 to 10 times annualized revenue.
50%UnverifiedThe key metrics for Micro-SaaS are monthly recurring revenue (MRR) and churn rate.
50%UnverifiedMRR (mean reciprocal rank) measures how early the first relevant result appears, taking the reciprocal of the rank of the first relevant document for each question and then averaging them, completely ignoring other relevant documents after the first.
50%UnverifiedRecall@k and MRR are commonly used to evaluate retrieval quality, while evaluating generation typically looks at the consistency between answers and sources, i.e., faithfulness.
50%UnverifiedMRR (Mean Reciprocal Rank) is the average of Reciprocal Rank over all answerable cases; it only takes the position of the first relevant result and does not care whether the remaining relevant documents were recalled.
50%UnverifiedWhen a question requires multiple pieces of evidence to be assembled, MRR may overestimate retrieval quality; in this case Recall@k should also be examined or metrics such as nDCG that reflect the overall quality of the ranking should be introduced.
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