REAL-TIME CONTENT RECOMMENDATION SYSTEM

摘要

System and methods of this disclosure are directed to recommending content in real-time or near real-time. The system comprises a number of pipelines updated a different frequencies that process temporally different sets of web property visit data. Within each pipeline, the system can employ different number of algorithms to process visit data to generate content recommendations. One algorithm is a content filter that filters from the visit data those determined to be unsuitable as recommendations. Another is a content analyzer that analyzes the content of each URL in the visit data by topic category and attribute. Another is an item-to-item collaborative filter that determines a correlation score for each URL based on those in the visit data in a single session Another is a device-to-item matrix factorization that determines an affinity score for each URL based on visit data, context information, and topic category.

主权项

1. A method of providing recommendations of real-time content, comprising:
selecting, by a first selection engine executing on a data processing system, a first set of content item identifiers from a first database of a content publisher at a first frequency; updating, by an update engine executing on the data processing system, a first pipeline with the first set of content item identifiers; selecting, by a second selection engine executing on the data processing system, a second set of content item identifiers from a second database of the content publisher at a second frequency, the second frequency different from the first frequency; updating, by the update engine, a second pipeline with the second set of content item identifiers; and producing, by an amalgamator engine executing on the data processing system, a combined set of content item identifiers comprising some of the first set of content item identifiers and some of the second set of content item identifiers.