Evaluation Predicting Device for Sparse Data Filtering
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Solution Overview
Problem
Existing filtering methods, such as collaborative and content-based filtering, face accuracy issues when dealing with a small number of users or items, and struggle to effectively recommend information due to low accuracy in predicting user preferences and item features.
Innovation Solution
An evaluation predicting device that uses latent vectors and Bayesian estimation to project known feature vectors into latent spaces, calculating posterior distributions to improve the accuracy of unknown evaluation value predictions and recommend items based on these calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If collaborative filtering is used, then filtering accuracy is improved under conditions of a large number of users and items, but filtering accuracy deteriorates under conditions of a small number of users or items
Solution Approach 1:
The patent combines collaborative filtering and content-based filtering into a unified evaluation prediction device. The collaborative filtering component uses user evaluation data to predict preferences, while the content-based filtering component analyzes item features and user preferences independently. By merging these two approaches, the system maintains high filtering accuracy under both large and small data conditions, as the content-based component can operate effectively even when collaborative filtering data is limited.
Solution Approach 2:
The invention creates a composite filtering system that integrates multiple filtering methodologies (collaborative filtering, content-based filtering, and evaluation prediction) into a single unified device. This composite structure allows the system to leverage the strengths of each individual method while compensating for their weaknesses, particularly improving performance in scenarios with limited user or item data by relying more heavily on content-based analysis.
2Adaptability or versatility
If content-based filtering is used, then filtering can operate with small numbers of users or items, but filtering accuracy deteriorates compared to collaborative filtering under large data conditions
Solution Approach 1:
The patent merges content-based filtering with collaborative filtering and evaluation prediction mechanisms. The content-based filtering component continues to operate independently on item features and user preferences, maintaining its advantage in small data scenarios, while the collaborative filtering component processes available evaluation data to improve accuracy when sufficient data exists. The evaluation prediction device integrates outputs from both approaches to produce final recommendations.
3Measurement precision
If filtering accuracy is improved through sophisticated methods, then information searchability is improved, but device complexity increases
Solution Approach 1:
The patent segments the filtering system into distinct functional modules: a collaborative filtering unit that processes user evaluation data, a content-based filtering unit that analyzes item features, and an evaluation prediction device that integrates both approaches. Each module operates independently with its own processing logic, making the overall complex system more manageable and maintainable while achieving high filtering accuracy through the coordinated operation of these segmented components.
Data Source
AI summary
Disclosed herein is an evaluation predicting device including: an estimating section configured to define a plurality of first latent vectors, a plurality of second latent vectors, evaluation values, a plurality of first feature vectors, a plurality of second feature vectors, a first projection matrix, and a second projection matrix, express the first latent vectors and the second latent vectors, and perform Bayesian estimation with the first feature vectors, the second feature vectors, and a known the evaluation value as learning data, and calculate a posterior distribution of a parameter group including the first latent vectors, the second latent vectors, the first projection matrix, and the second projection matrix; and a predicting section configured to calculate a distribution of an unknown the evaluation value on a basis of the posterior distribution of the parameter group.