Mhout Collaborative filtering algorithm for each interface

Mahout Collaborative filtering algorithm

Mahout uses taste to improve the implementation of the collaborative filtering algorithm, which is an extensible and efficient recommendation engine based on Java implementation. Taste not only realizes the most basic user-based and content-based recommendation algorithm, but also provides an extension interface, so that users can easily define and implement their own recommendation algorithm. At the same time, taste is not just for Java applications, it can provide recommended logic to the outside world as a component of an internal server, in the form of HTTP and Web service. Taste's design enables it to meet the requirements of the enterprise for the performance, flexibility and scalability of the recommendation engine.

Taste mainly includes the following interfaces:

• Datamodel is an abstract interface for user preferences, and its implementation enables the extraction of user preferences from any type of data source. Taste provides Jdbcdatamodel and Filedatamodel by default and supports reading user preferences from databases and files, respectively.
• Usersimilarity and Itemsimilarity. Usersimilarity is used to define the similarity between the two users, which is the core of the recommendation engine based on collaborative filtering, which can be used to calculate the "neighbor" of a user, where we will call a user who is similar to the current user's neighbor. Itemsimilarity Similar, calculates the similarity between the item.
• Userneighborhood the recommended method for user-based similarity, the recommended content is based on finding a neighbor user who is similar to the current user preferences. Userneighborhood defines a method for determining the neighbor's user, which is generally based on usersimilarity computation.
• Recommender is the abstraction interface for the recommended engine, the core component in taste. program, it provides a datamodel that can calculate the recommended content for different users. In practical application, it mainly uses its implementation class Genericuserbasedrecommender or Genericitembasedrecommender to implement the recommendation engine based on user similarity or content-based recommendation engine respectively.
• Recommenderevaluator: the rating device.
• Recommenderirstatsevaluator: Collect recommended performance-related metrics, including accuracy, recall, and more.

Mhout Collaborative filtering algorithm for each interface