Item Recommendation System with Structured Sharing Networks
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Solution Overview
Problem
Current social applications and review sites lack an efficient method for storing, sharing, and recommending specific items such as products, places, or experiences, as actionable answers are often lost in full text responses, and individuals struggle to recall relevant information like book titles, favorite places, or recommendations.
Innovation Solution
A person-to-person item recommendation system that allows users to maintain lists of items, share them directly or through social media, and mark items as recommended, creating a meta-catalog for deduplicated items with associated information, tracking sharing and recommendation actions to establish relationships and patterns of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If individuals use existing social applications or review sites to exchange recommendations, then information can be shared, but actionable answers are lost in full text responses and individuals cannot instantly recall relevant items
Solution Approach 1:
The patent extracts actionable information from full text responses by creating structured item profiles that separate key recommendations (products, places, experiences) from descriptive text. This extraction process isolates the essential actionable answers into machine-readable formats that can be easily shared and recalled, resolving the contradiction between preserving information quality and improving exchange efficiency
Solution Approach 2:
The system creates structured copies of recommendations in a standardized format that preserves the essential actionable information while enabling efficient sharing. Instead of exchanging full text responses, individuals share structured item data that can be instantly processed and displayed, maintaining information integrity while improving operational efficiency
2Loss of time
If individuals rely on memory to recall relevant items when asked questions, then no external system is needed, but individuals cannot immediately recall specific item names or details
Solution Approach 1:
The patent implements preliminary action by having individuals proactively save and structure their recommendations, experiences, and favorite items into the system before they are needed. This advance preparation creates an organized repository of information that can be instantly retrieved when questions arise, eliminating both the time loss and accuracy problems associated with spontaneous recall
3Loss of information
If a system stores detailed information about items and sharing actions, then recommendation value is enhanced, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the information architecture into distinct modular components: item profiles (with standardized attributes), user profiles, sharing networks (relationship graphs), and action logs. This segmentation allows the system to store detailed information about items and sharing actions without creating monolithic complexity, as each component can be independently managed and queried
Data Source
AI summary
A system in which a sharing network is determined through the sharing and acceptance of items by individuals. Individuals are able to maintain lists of items of interest in an account, such as books, restaurants, hotels, clothes, etc. The system allows an individual to share an item in a list with another individual, and the recipient is able to accept or decline the share. If the recipient accepts the share, a sharing network is established between the individuals. An individual is able to view recommended items in the accounts of other individuals who are within their sharing network. The establishment of sharing networks and the associated distribution of items between individuals can be analyzed to determine the strength of the bond between two or more individuals.