Query Analyzer for Personalized Product Recommendations
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Solution Overview
Problem
Conventional online shopping platforms fail to consider user personality and search motivation, leading to inefficient and ineffective product retrieval, as they do not differentiate between customers' preferences, resulting in unsuitable search results for both general and specific queries.
Innovation Solution
An online shopping platform with a query analyzer that determines whether a query is general or specific, using a general retrieval module to infer customer preference based on user behavior and a specific retrieval module to group items by cost performance, providing personalized and relevant search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional item retrieval systems return the same results for every customer based on objective facts, then the system maintains simplicity and consistency, but it fails to account for individual customer preferences and personalities, resulting in inefficient shopping experiences
Solution Approach 1:
The system performs preliminary actions by inferring customer preferences and personalities before the actual shopping task. Preference inference components analyze customer behavior patterns, purchase history, and interaction data in advance to establish personalized profiles, which then guide the retrieval process without adding complexity during the actual search operation
Solution Approach 2:
The patent introduces intermediary components including preference inference modules, personality detection algorithms, and ranking adjustment mechanisms that act as mediators between the customer query and the item retrieval process. These intermediaries translate objective item facts into personalized results by applying customer-specific filters and weighting schemes
2Adaptability or versatility
If the system provides comprehensive listings with popular items for general queries, then it ensures broad coverage, but it fails to provide personalized recommendations tailored to individual customer preferences and motivations
Solution Approach 1:
The system implements feedback mechanisms where customer interactions with search results, purchase history, and behavioral patterns are continuously analyzed to refine preference inferences. The personality detection component uses feedback from customer responses to adjust ranking parameters and provide increasingly accurate personalized recommendations over time
Solution Approach 2:
The patent dynamically changes retrieval parameters based on inferred customer personality traits and preferences. Different personality types trigger different ranking functions, filtering criteria, and result presentation formats, allowing the same comprehensive database to serve diverse customer needs through parameter adjustment rather than information loss
3Ease of operation
If conventional retrieval systems rank items based on objective metrics like release date and turnover, then the ranking process remains transparent and consistent, but it cannot accommodate subjective customer preferences such as brand loyalty or price sensitivity
Solution Approach 1:
The system transitions from static, fixed ranking criteria to dynamic, adaptable ranking mechanisms. The ranking function adjusts in real-time based on the customer's inferred personality type and current preferences, allowing the same item to be ranked differently for different customers while maintaining operational efficiency through automated dynamic adjustment
Data Source
AI summary
Disclosed herein are technologies for providing recommendations as to particular products and/or services that are customer specific and general, based on customer preference and inquiry. The recommendations are provided as part of an online shopping system. In accordance with one aspect, an item query is received from a customer, and analyzed by a query analyzer to determine if the query is a general item query or a specific item query. A search may be performed for items based on the item query in an items database listing items offered for purchase. If the query is the general item query, customer preference is determined from results of the search. If the query is the specific item query, the items from the results of the search are grouped based on cost performance. The items of the search result are ranked and provided to the customer.