Prediction Aggregation Mechanism for Consumer Satisfaction Forecasting
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Solution Overview
Problem
Consumers face challenges in finding suitable goods and services that meet their specific needs due to the limitations of existing rating systems and collaborative filtering technologies, which often require significant effort and may be influenced by vendor incentives, leading to inadequate information and potential misrepresentation.
Innovation Solution
A prediction aggregation mechanism that uses input from consumers and independent predictors with financial or reputational incentives to provide customized satisfaction assessments for specific offers, aggregating predictions to help consumers make informed decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If collaborative filtering technologies are used to customize recommendations, then recommendations are customized to customer preferences, but the system does not work well for new products or products in categories with insufficient buying history data
Solution Approach 1:
The patent introduces an intermediary assessment mechanism that bridges the gap between collaborative filtering and new product evaluation. Independent assessors with relevant expertise evaluate new products directly, providing reliable data that can then be integrated into the collaborative filtering system. This intermediary layer enables accurate predictions for new products without relying solely on historical buying data.
Solution Approach 2:
The system performs preliminary assessments of new products by independent evaluators before they enter the main collaborative filtering pipeline. This preliminary action generates initial quality signals and expertise-based evaluations that prepare the data for subsequent collaborative filtering processing, enabling the system to handle new products effectively from the start.
2Ease of operation
If customers rely on advice from retail salespeople, then they receive personalized product recommendations, but the advice may be biased by salespeople's incentives to maximize employer profit rather than consumer satisfaction
Solution Approach 1:
The patent extracts the assessment function from the sales context by introducing independent assessors who are separated from vendor incentives. These assessors evaluate products based on objective criteria and customer needs without pressure to maximize sales or employer profit. This extraction of the evaluation function from the sales function eliminates the conflict of interest inherent in traditional retail advice.
Solution Approach 2:
Independent assessors serve as intermediaries between customers and products, providing unbiased recommendations that are not influenced by vendor incentives. These intermediaries translate product features into customer value propositions without the conflicting interests that plague traditional salespeople, thereby improving the reliability of product advice.
3Reliability
If feedback mechanisms like seller feedback scores are used, then customers can identify reliable vendors, but customers still struggle to choose among many competing vendors with similar feedback profiles and need additional means to identify specific products that fit their needs
Solution Approach 1:
The patent applies local quality by providing vendor-specific and product-specific assessments that go beyond generic feedback scores. Independent assessors evaluate each vendor's performance on specific product categories and individual products, creating differentiated quality signals that allow customers to distinguish between vendors with similar overall feedback profiles. This localized assessment approach reveals nuanced differences that aggregate scores mask.
4Loss of information
If rating institutions assess particular products, then consumers can find information on satisfaction with particular products, but the overall ratings do not generally seek to customize recommendations for particular customers
Solution Approach 1:
The patent introduces dynamics by making recommendations adaptive to individual customer needs and contexts. Independent assessors evaluate products in relation to specific customer requirements, and the system dynamically adjusts recommendations based on customer profiles, purchase history, and stated preferences. This transforms static product ratings into dynamic, personalized recommendations that adapt to each customer's unique situation.
Data Source
AI summary
A method and structure for predicting a satisfaction a consumer will experience contingent on accepting one or more offers from potential sellers. A request is received from a consumer describing one or more transactions in which the consumer may wish to engage. One or more offers are received from one or more potential sellers in response to the request. One or more predictive assessments are received from one or more predictors corresponding to one or more of these offers, each predictive assessment predicting a satisfaction that the consumer will experience contingent on accepting one or more offers from the potential sellers. For one or more of these offers, at least one aggregated prediction is calculated, based on the corresponding predictive assessments, according to a prediction aggregation mechanism.