Shulou(Shulou.com)05/31 Report--

This article mainly introduces "what are the advantages of Mahout's engine Taste". In daily operation, I believe many people have doubts about the advantages of Mahout's engine Taste. The editor consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful to answer the question of "what are the advantages of Mahout's engine Taste?" Next, please follow the editor to study!

Taste is an efficient implementation of a personalized recommendation engine provided by Apache Mahout. The engine is based on java and has strong scalability. At the same time, some recommendation algorithms are transformed into MapReduce programming mode in mahout, so that the distributed architecture of hadoop can be used to improve the performance of recommendation algorithms.

Taste in the Mahout0.5 version implements a variety of recommendation algorithms, including the most basic user-based and content-based recommendation algorithms, the more efficient SlopeOne algorithm, and the algorithm based on SVD and linear interpolation in the research stage. At the same time, Taste also provides an extended interface for customized development of personalized recommendation algorithms based on content or model.

Taste is not only suitable for Java applications, but also provides recommended logic to the outside world in the form of HTTP and Web Service as a component of the internal server. The design of Taste enables it to meet the requirements of enterprise recommendation engine in terms of performance, flexibility and scalability.

The following figure shows the core components that make up Taste:

As you can see from the above figure, Taste consists of the following main components:

DataModel:DataModel is an abstract interface for user preference information, and its implementation supports the extraction of user preference information from specified types of data sources. In Mahout0.5, Taste provides two types of implementations, JDBCDataModel and FileDataModel, which support reading users' preferences from databases and file systems, respectively. For database reading support, Mahout 0.5 only provides support for MySQL and PostgreSQL, if the data is stored in other databases, or imported into these two databases, or self-programming to implement the corresponding classes.

UserSimilarit and ItemSimilarity: the former is used to define the similarity between two users, and the latter is used to define the similarity between two projects. Mahout supports most resident similarity or correlation calculation methods, and it is necessary to choose a reasonable similarity calculation method for different data sources.

UserNeighborhood: in the user-based recommendation approach, the recommended content is based on finding "neighbor users" that are similar to the current user's preferences, and this component is used to define "neighbor users" adjacent to the target user. Therefore, this component is only used in user-based recommendation algorithms.

Recommender:Recommender is the abstract interface of recommendation engine and the core component of Taste. Using this component, you can generate a list of project recommendations for specified users.

At this point, the study on "what are the advantages of Taste, the engine of Mahout" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.