Pre-Qualifying Network Resources for Recommendation Systems
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Solution Overview
Problem
Existing recommendation systems face inefficiencies due to the high computational resources required for real-time content selection, particularly when dealing with a large volume of potential content sources, which can lead to slow processing and increased resource consumption.
Innovation Solution
A method and apparatus that pre-qualify network resources as potential content sources offline using a machine learning algorithm, prioritizing resources with fast-paced content updates, such as news aggregators, and combining user-specific and non-specific content sources to enhance recommendation diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time content selection is performed for all potential content sources, then recommendation accuracy is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing quality parameters for content sources during an offline training phase. The machine learning algorithm processes historical data to evaluate content source quality in advance, storing results in a database. During real-time recommendation, the system directly retrieves pre-computed quality parameters instead of performing intensive calculations, thereby maintaining high recommendation accuracy while significantly reducing online computational resource consumption.
2Speed
If content sources are pre-qualified offline based on update frequency, then real-time processing speed is improved, but the complexity of the selection algorithm increases
Solution Approach 1:
The patent transforms the complex multi-dimensional content source evaluation into a simplified parameter-based system. It identifies key parameters such as update frequency, content quality metrics, and relevance scores, then uses a machine learning model to compute a composite quality parameter offline. This approach maintains high real-time processing speed by reducing online calculations to simple parameter lookups, while the increased offline algorithm complexity is acceptable since it occurs during non-critical training periods.
3Adaptability or versatility
If all network resources are evaluated as content sources, then content diversity is improved, but processing time increases
Solution Approach 1:
The system performs preliminary evaluation of all potential network resources offline, computing quality parameters and relevance scores in advance. During real-time operation, it quickly filters and selects content sources based on pre-computed metrics, maintaining content diversity by considering multiple qualified sources while dramatically reducing processing time. The offline phase evaluates comprehensive resource characteristics, and the online phase efficiently retrieves and combines results from diverse pre-qualified sources.
Data Source
AI summary
There are disclosed a method of and a system for selecting a network resource as a source of a content item, the content item to be analyzed by a recommendation system as part of a plurality of content items to generate a set of recommended content items as a recommendation for a given user of the recommendation system. The method comprises, for a network resource, receiving, by the server, a plurality of features associated with a network resource to be processed; generating given network resource profile for the network resource, the given network resource profile being based on the plurality of features; executing a machine learning algorithm in order to determine a source suitability parameter for the network resource, selecting at least one content item from the network resource if the source suitability parameter is determined to be above a pre-determined threshold.