Noise Filtering in Recommendation Systems
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Solution Overview
Problem
Current recommendation systems are susceptible to noise in training data, leading to poor quality recommendations due to issues like insufficient data, missing data, and patterns in user behavior, which affects user experience and revenue.
Innovation Solution
A method that involves building knowledge representations of elements with tagentiblity and hierarchy properties, using implicit and explicit attributes, and applying feedback optimization techniques to reduce noise by adjusting relevancy attribute weights based on positive and negative feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If association mining algorithms are used to generate recommendations, then a probabilistic bundle of items can be provided, but the recommendations become susceptible to noise in the training data
Solution Approach 1:
The patent segments the recommendation generation process into multiple independent modules: noise detection module, noise filtering module, and recommendation generation module. This segmentation allows each module to specialize in its function, with the noise detection module specifically identifying noisy elements and the filtering module removing them before recommendations are generated, thus improving reliability without sacrificing productivity
Solution Approach 2:
The patent introduces an intermediary noise filtering layer between the training data and the recommendation algorithm. This intermediary component processes the training data to remove noise before it reaches the association mining algorithm, preventing noise propagation while maintaining the algorithm's efficient recommendation generation capabilities
2Reliability
If more training data is collected to improve recommendation quality, then the system can handle more patterns in user behavior, but the system becomes bound by the quality and completeness of the input data
Solution Approach 1:
The patent applies preliminary action by performing noise detection and filtering on training data before it is used for model training. This preprocessing step ensures that only high-quality, noise-free data is fed into the recommendation system, improving reliability without requiring complex post-processing or additional computational resources during inference
Solution Approach 2:
The noise filtering system performs self-service by automatically detecting and removing noise from training data without requiring manual intervention. The system uses automated algorithms to identify noisy elements based on statistical properties and patterns, reducing the need for human expertise in data cleaning while maintaining high recommendation quality
Data Source
AI summary
In one aspect, a method of forming a plurality of elements with tagentiblity and hierarchy properties, the method comprising the steps of: building a set of knowledge representations wherein the step of building the set of knowledge representations further comprises: wherein the plurality of elements are extracted from a domain information and are a meaning the domain information; associating each individual elements of the plurality of elements into a grouping of elements using a plurality of attributes comprising an implicit attribute and an explicit attributes, utilizing a plurality of knowledge sources and a plurality of complimentary knowledge sources that prior collected prior in an incremental manner, and associating the plurality of elements of claim into the plurality of knowledge sources using a relevancy technique; applying a feedback optimization technique to the plurality of knowledge sources; decreasing a relevancy attribute weights of the plurality of attributes by a decay on a negative feedback on elements; increasing the relevancy attribute weights of the plurality of attributes by a decay on positive feedback on the plurality of elements; and finding a set of differing attributes and increasing the relevancy attribute weights of the set of differing attributes by a decay on substitution feedback on the plurality of elements.