A technique used in recommendation systems that predicts user preferences based on past interactions of similar users or items.

A recommendation method that suggests items similar to those a user has liked in the past, based on item features.

A metric that measures the accuracy of positive predictions in a recommendation system, calculated as true positives divided by all positive predictions.

A metric that indicates the ability of a recommendation system to find all relevant items, calculated as true positives divided by all actual relevant items.

A metric that combines precision and recall into a single score, balancing both metrics for a comprehensive evaluation.

Quantitative measures used to assess the performance of recommendation systems, including precision, recall, and F1-score.

The process of enhancing user satisfaction by improving the usability and accessibility of a recommendation system's interface.

The technique of selecting a subset of data from a larger dataset to analyze and optimize recommendation algorithms.

A process of reducing the number of features in a dataset while preserving essential information, often used to improve model performance.

The process of making a recommendation system accessible online through web applications using frameworks like Flask or Django.

A lightweight web framework for Python that is often used to build web applications, including those integrating machine learning models.

A high-level Python web framework that encourages rapid development and clean, pragmatic design for web applications.

A method to evaluate how easy and user-friendly a recommendation system's interface is by observing real users.

The process of creating a visual guide to the structure of a web application, outlining its layout and functionality.

Developing a preliminary model of a recommendation system's interface to test concepts and gather user feedback.

Input from users regarding their experience with a recommendation system, used to inform design and functionality improvements.

Techniques used to manage errors in a web application, ensuring a smooth user experience even when issues arise.

An algorithm that learns patterns from data to make predictions or recommendations, forming the core of a recommendation system.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

The process of optimizing the parameters of a machine learning model to improve its performance.

A software system that suggests products or content to users based on data analysis and algorithms.

A method that recommends items similar to those a user has previously liked based on item similarity.

A method that recommends items based on the preferences of similar users.

A method of evaluating a recommendation system's performance by comparing it against a simple or standard model.

A table used to evaluate the performance of a classification model, showing true vs. predicted classifications.

The steps taken to clean and organize data before it is used in a recommendation system, crucial for model accuracy.