Search results
Results from the WOW.Com Content Network
Active users is a software performance metric that is commonly used to measure the level of engagement for a particular software product or object, by quantifying the number of active interactions from users or visitors within a relevant range of time (daily, weekly and monthly).
Evaluations measures are used in studies of information behaviour, usability testing, business costs and efficiency assessments. Measuring the effectiveness of IR systems has been the main focus of IR research, based on test collections combined with evaluation measures. [5]
It is based on the probabilistic retrieval framework developed in the 1970s and 1980s by Stephen E. Robertson, Karen Spärck Jones, and others. The name of the actual ranking function is BM25 . The fuller name, Okapi BM25 , includes the name of the first system to use it, which was the Okapi information retrieval system, implemented at London ...
Other metrics such as MAP, MRR and precision, are defined only for binary judgments. Recently, there have been proposed several new evaluation metrics which claim to model user's satisfaction with search results better than the DCG metric: Expected reciprocal rank (ERR); [12] Yandex's pfound. [13]
The metrics reference model (MRM) is the reference model created by the Consortium for Advanced Management-International (CAM-I) to be a single reference library of performance metrics. This library is useful for accelerating to development of and improving the content of any organization's business intelligence solution.
The trust metrics will help you quickly find the best model for your task or find important areas where you can make improvements to your model. Like many areas of machine learning, this is a work ...
The user based top-N recommendation algorithm uses a similarity-based vector model to identify the k most similar users to an active user. After the k most similar users are found, their corresponding user-item matrices are aggregated to identify the set of items to be recommended.
Define the metrics that will be able to help you answer the question. A proper cohort analysis requires the identification of an event, such as a user checking out, and specific properties, like how much the user paid. The gaming example measured a customer's willingness to buy gaming credits based on how much lag time there was on the site.