Recommendation Engine Using Social Review Data for Consumer Preference Detection

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Solution Overview

Problem

Current techniques fail to accurately detect consumer attitudes and values from online behavior without human intervention, posing challenges in predicting consumer preferences effectively amidst increasing commercial competition and the need for cost reduction and efficiency.

Innovation Solution

A recommendation engine system that integrates customer social review-based data to understand preferences by analyzing customer transaction history and social media reviews using natural language machine learning models, enabling automated detection of purchasing behavior and sentiment analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated detection of consumer attitudes and values from online behavior is implemented, then productivity and efficiency are improved, but measurement precision and reliability of consumer preference detection deteriorate

Engineering Contradiction:

ImproveefficiencyVSAvoidaccuracy of consumer attitude detection

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent introduces social review data as an intermediary medium to bridge the gap between automated processing and accurate consumer attitude detection. Instead of directly analyzing raw consumer behavior data which lacks contextual understanding, the system uses social reviews as a mediator that captures consumer sentiments, preferences, and values in a structured format that can be reliably processed by machine learning models.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual human analysis of consumer behavior with automated machine learning models that process social review data. This substitution of mechanical (human) analysis with automated systems improves productivity while maintaining measurement precision through sophisticated algorithms that can accurately detect consumer attitudes from textual social review data.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If human intervention is used to analyze consumer behavior, then measurement precision improves, but device complexity and operational difficulty increase

Engineering Contradiction:

Improveaccuracy of consumer preference detectionVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent implements a self-service system where machine learning models automatically analyze social review data without requiring human intervention. The system self-processes consumer behavior data, extracts insights, and generates recommendations autonomously, thereby maintaining measurement precision while reducing system operational complexity and eliminating the need for manual analysis.

Inventive Principle:

Principle #25Self-service

3Reliability

If comprehensive analysis of consumer online behavior is performed, then reliability of preference prediction improves, but loss of time and computational resources increase

Engineering Contradiction:

Improveaccuracy of preference predictionVSAvoidtime for data processing

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent extracts and focuses analysis on the most relevant data - social review data that directly reflects consumer attitudes and values. Instead of processing all available online behavior data which is voluminous and time-consuming, the system selectively extracts and analyzes social review content that provides the most reliable insights into consumer preferences, thereby improving prediction reliability while reducing processing time.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent performs preliminary processing of social review data to pre-identify key consumer attitudes and values before final preference prediction. This preliminary action involves pre-processing and structuring the social review data in advance, which speeds up the subsequent analysis and reduces the time required for comprehensive preference prediction while maintaining high reliability.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11023953B1Recommendation engine that integrates customer social review-based data to understand preferences and recommend products

Publication Date: 2021.06.01 CAPITAL ONE SERVICES LLC

AI summary

The disclosure describes a system and methods for implementing a recommendation engine. The recommendation engine can at least generate a segmentation identifying a customer group for a product, receive a customer review from a storage location, generate a customer review profile based on the customer review, match the customer review profile to the customer group based on comparing purchase factors, preference levels, or a combination thereof associated with the segmentation and purchase factors, preference levels, or a combination thereof associated with the customer review profile, and recommend the product, one or more features of the product, or a combination thereof to a further customer based on the matched customer review profile.