Recommendation System Using Fourier Transform Clustering
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Solution Overview
Problem
Existing techniques face difficulties in adapting time series data to generate recommendations for subjects, particularly when the data sets differ in length, making it challenging to measure similarities and determine suitable ratings for analysis.
Innovation Solution
A clustering algorithm is applied to segment data subjects into groups based on their time series data, allowing for the assignment of implicit ratings, which are then used by a neural network model to generate recommendations with actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If time series data of varying lengths is used for recommendation generation, then the system can handle diverse data subjects with different observation periods, but measuring similarities between data subjects becomes challenging
Solution Approach 1:
The patent transforms time series data into frequency domain representations using Fourier transforms, changing the parameter space from time-domain lengths to frequency-domain characteristics. This allows data subjects with different observation periods to be compared based on their spectral features rather than temporal duration, resolving the similarity measurement challenge while maintaining adaptability to varying data lengths
Solution Approach 2:
The patent generates implicit ratings by comparing each data subject against multiple reference profiles, using disposable reference vectors that are created and discarded for each comparison. This approach enables flexible handling of varying data lengths without requiring persistent, complex similarity structures, as each reference profile is independently constructed and used solely for its intended comparison purpose
2Loss of information
If clustering algorithms are applied to segment data subjects into groups, then implicit ratings can be determined for each group, but the complexity of the recommendation system increases
Solution Approach 1:
The patent segments data subjects into distinct groups based on their time series characteristics using clustering algorithms. This segmentation preserves the unique characteristics of different data subjects by organizing them into meaningful categories, while the modular structure of the segmentation process helps manage system complexity through organized, reusable components
Solution Approach 2:
The patent creates reference profiles that serve multiple functions: they act as cluster centers for segmentation, provide basis vectors for similarity comparisons, and enable implicit rating generation. This multi-functionality reduces system complexity by eliminating the need for separate structures for each of these operations, as the same reference profiles fulfill all three roles
3Measurement precision
If implicit ratings are determined based on clustered groups, then recommendations can be generated for data subjects, but handling data sets of different lengths becomes more difficult
Solution Approach 1:
The patent converts time series data from the time domain to the frequency domain using Fourier transforms before clustering. This parameter transformation allows the clustering algorithm to operate on frequency characteristics rather than temporal sequences, enabling accurate rating determination while maintaining adaptability to data sets of different lengths, as frequency representations are invariant to the original time series duration
Solution Approach 2:
The patent introduces frequency domain representations as an intermediary between the raw time series data and the clustering algorithm. This intermediary transformation layer allows the system to handle varying data lengths effectively by converting temporal information into frequency characteristics that can be meaningfully compared across different observation periods while preserving the information needed for accurate implicit rating determination
Data Source
AI summary
A recommendation system generates recommendations for data subjects. A plurality of time series data that includes a set of time series data corresponding to each data subject of a plurality of data subjects are received. Data subjects are segmented into groups according to a clustering algorithm applied to the sets of time series data. Implicit ratings for the data subjects are determined. The implicit ratings include an implicit rating determined for each data subject based on a group into which the data subject is segmented. A recommendation for a first data subject of the plurality of data subjects is generated using a neural network model based on the determined implicit ratings. The recommendation includes an actionable insight associated with the first data subject. In a further example, the neural network model is a deep recommendation model.