Decision Engine Weighted Criteria Recommendation System
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Solution Overview
Problem
Current e-commerce systems fail to effectively collect and present comprehensive information about products or services, leading to difficulties in comparing competing options and often result in irrelevant advertisements, as they do not centrally manage all available data and prioritize user preferences effectively.
Innovation Solution
A system utilizing a decision engine that processes preference information, including activated decision-making criteria and corresponding weight values, to provide personalized and population-based recommendations by calculating scores for options, allowing users to interact with and adjust criteria for tailored results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If current e-commerce systems provide recommendations based on user purchases, then users receive product recommendations, but spamming occurs when users receive too much irrelevant advertisement
Solution Approach 1:
The system changes the parameters of recommendation generation by introducing decision-making criteria with weight values. Instead of relying solely on purchase history, the system uses weighted criteria (e.g., price importance, brand preference, feature priorities) to filter and rank products, thereby reducing irrelevant advertisements while maintaining recommendation effectiveness.
Solution Approach 2:
The system incorporates feedback mechanisms where users can indicate their preference for certain criteria, and this feedback is used to adjust future recommendations. The decision-making criteria are refined based on user interactions, ensuring recommendations become more relevant over time and reducing spamming behavior.
2Quantity of substance
If e-commerce systems collect information from multiple sources, then more comprehensive product information is available, but the information is not centralized and comparison becomes difficult
Solution Approach 1:
The system merges information from multiple sources (product specifications, user reviews, expert evaluations, pricing data) into a centralized structure organized by decision-making criteria. This consolidation allows users to compare products systematically across all relevant attributes rather than navigating scattered information across multiple pages or sources.
Solution Approach 2:
The system segments the comprehensive product information into distinct decision-making criteria categories (e.g., price, performance, features, reliability). Each criterion is independently evaluated and weighted, making it easier for users to compare products based on their priorities while maintaining access to comprehensive information across all segments.
3Loss of information
If the system presents all available product information, then users have complete data for comparison, but the presentation is ineffective and comparisons among competing products become difficult
Solution Approach 1:
The system applies local quality by tailoring the presentation of information to match user-specific decision-making criteria and weight values. Rather than presenting all product information uniformly, the system highlights and organizes information locally according to what matters most to each user, making comparisons more effective while maintaining completeness through the structured criteria framework.
Data Source
AI summary
The embodiments provide a system for decision-making criteria-based recommendations. The system may include a decision engine configured to receive a request for recommendations for an option problem associated with a product or service category, and determine options among a plurality of options for the product or service category based on preference information. The preference information may include activated decision-making criteria and corresponding weight values. The corresponding weight values may represent a relative importance of each activated decision-making criterion. The decision engine may be configured to determine options among the plurality of options including calculating scores for the plurality of options based on, in part, the activated decision-making criteria and the corresponding weight values and selecting the options among the plurality of options based on the calculated scores. The decision engine may be configured to provide a display of the determined options as the recommendations for the product or service category.