Intelligent Dialog System for Personalized Product Recommendations
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Solution Overview
Problem
Current search technologies struggle to provide relevant, personalized product recommendations to consumers without overwhelming them with irrelevant information, especially when they are unsure of the specific product they need, leading to inefficiencies in e-commerce and product discovery.
Innovation Solution
A system and method utilizing a graph database and artificial intelligence to manage a dialog with consumers, employing forward and backward chaining logic to ask targeted questions and infer product recommendations based on unstructured consumer input, weighing attributes and facts to suggest relevant products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If collaborative filtering is used to target advertising and search results based on consumer characteristics, then personalization is improved, but the quantity of irrelevant results increases
Solution Approach 1:
The patent segments the large set of search results into personalized subsets based on consumer characteristics from collaborative filtering. By dividing results into relevant and irrelevant portions and presenting only the personalized subset, the system maintains personalization while reducing the quantity of results the consumer must review.
Solution Approach 2:
The system applies partial action by providing only the portion of results that are personally relevant to the consumer rather than the complete set. This selective presentation reduces information overload while maintaining the personalization benefit of collaborative filtering.
2Loss of information
If traditional search engines return large numbers of results to satisfy various queries, then completeness is improved, but the time required to find relevant products increases
Solution Approach 1:
The system performs preliminary action by pre-filtering and ranking results based on consumer characteristics before presentation. Collaborative filtering algorithms pre-process the result set to identify personally relevant items, so consumers receive a pre-sorted list that requires minimal additional filtering time.
Solution Approach 2:
The system uses feedback from consumer behavior patterns captured by collaborative filtering to continuously improve result ranking. By analyzing what consumers actually click on and purchase, the system refines its ability to prioritize relevant results, reducing search time while maintaining completeness.
3Measurement precision
If consumers filter through numerous results or form new queries to find relevant products, then accuracy of product match is improved, but productivity decreases
Solution Approach 1:
The system performs self-service by automatically filtering and ranking results according to consumer preferences without requiring manual filtering actions. The collaborative filtering system autonomously analyzes consumer behavior and adjusts result presentation, maintaining high accuracy while eliminating the time-consuming manual filtering process.
Data Source
AI summary
Systems and methods for providing a recommendation are disclosed. A method includes: presenting a first question via a user interface; receiving a response to the first question via the user interface; determining a second question using the received response to the first question and a weighted collection of attributes corresponding to a plurality of items; presenting the determined second question via the user interface; receiving a response to the second question via the user interface; determining at least one recommended item from the plurality of items based on the response to the first question, the response to the second question, and the weighted collection of attributes corresponding to the plurality of items; and presenting the determined at least one recommended item via the user interface.