Unique Item Recommendation Engine Using Segmentation and Scoring

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Solution Overview

Problem

Collaborative filtering systems are ineffective in recommending unique items as they assume all similar items are identical, failing to account for variations in dynamic or unique items such as used vehicles and homes, which have different features and conditions.

Innovation Solution

A system that includes a data collection engine to monitor user devices, a scoring engine to generate similarity scores, and a user penalty calculator to provide user-specific recommendations, allowing for real-time generation of recommendations based on item features and historical user preferences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If collaborative filtering systems assume all similar items are identical, then the system complexity is reduced and processing is simplified, but the recommendation accuracy for unique items deteriorates

Engineering Contradiction:

Improvesystem complexityVSAvoidrecommendation accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent segments items into standardized attributes and features that can be independently compared. Each unique item is broken down into discrete characteristics (e.g., specifications, properties, attributes) that can be systematically evaluated and matched, allowing the system to handle item uniqueness without overwhelming complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes parameters by introducing weighted attribute matching and similarity scoring mechanisms. Instead of treating items as identical or completely unique, the system evaluates multiple parameters simultaneously with different weights, transforming the recommendation approach from binary (same/different) to continuous (degree of similarity)

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If the system considers multiple features and user-specific preferences for unique items, then recommendation accuracy improves, but the computational complexity and processing time increase

Engineering Contradiction:

Improverecommendation accuracyVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-processing and standardizing item attributes before comparison. User preferences are pre-weighted and stored as profiles, and item features are normalized into comparable formats in advance, reducing the computational burden during real-time recommendation generation

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates simplified representations or copies of complex item data in standardized formats. By maintaining structured attribute profiles and similarity score caches, the system avoids repeatedly processing full item datasets, reducing computational complexity while preserving recommendation accuracy

Inventive Principle:

Principle #26Copying

Data Source

PatentUS10963942B1Systems, methods, and devices for generating recommendations of unique items

Publication Date: 2021.03.30 VAST COM

AI summary

The disclosure herein provides systems, methods, and devices for generating recommendations of dynamic or unique items. A system for generating recommendations of unique items comprises a data collection engine, a scoring engine, a user penalty calculator, and a recommendation compilation engine, wherein the recommendation engine is configured to transmit to a user access point system a list of recommended alternative unique items predicted to be preferred by a user of the user access point system.