Recommendation engine based on interest graph

Abstract

The invention discloses a recommendation engine based on an interest graph. The recommendation engine calculates and obtains association of objects and users through data digging by combining multi-dimension by means of an open source project Mahout through a weight factor allocation recommendation algorithm, and recommends intelligently for the users through weight dynamic adjustment and fine adjustment data feedback. The recommendation engine comprises a recommendation module and a recommendation context integrating module. The recommendation module comprises a Mahout user preference recommendation module, an attribute recommendation knowledge module based on the users and a system allocation context recommendation module. The recommendation context integrating module comprises a weight factor integrating context module, a Pylons Controller user recommendation Api interface, a user registering and inquiring interface, and a Pylons View layer. According to the recommendation engine, the loading efficiency of system performance is maximized, and the engine recommends topics , tasks and problems for the users intelligently according to interests of the users, so that the better user experience is realized.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a recommendation engine based on an interest graph. Background technique [0002] Machine learning is a branch of artificial intelligence that involves techniques that allow a computer to improve its output based on previous experience. This field is closely related to data mining and often requires the use of techniques including statistics, probability theory, and pattern recognition. While machine learning is not a new field, its pace of development is undeniable. Many large companies, including IBM, Google, Amazon, Yahoo!, and Facebook, have implemented machine learning algorithms in their applications. In addition, there are many companies that apply machine learning in their applications in order to learn from users and their past experiences to reap the benefits. [0003] In the information age, the success of companies and individuals increasingly depends on quick...

AI Technical Summary

Problems solved by technology

In the absence of a recommendation system, it is usually managed in a unified manner when recommending to users, coupled with some complex data table management to obtain results, and give real-time feedback to users, which will cause users to load recommended content. The system loads slowly, and the recommended content lacks directionality, resulting in a decline in user experience

Images