Person-to-Person Item Recommendation System with Structured Sharing
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Solution Overview
Problem
Current social applications and review sites lack an efficient method for exchanging and recalling specific recommendations for items like books, restaurants, or travel destinations, as actionable answers are often lost in text responses.
Innovation Solution
A person-to-person item recommendation system that allows users to maintain lists of items, share them, and recommend them to others, with features like deduplicated meta-catalogs, sharing networks, and tracking of item actions to map patterns of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If text-based responses are used to share recommendations, then information can be exchanged, but actionable answers are lost and recall becomes difficult
Solution Approach 1:
The system creates structured copies of recommendation data with specific fields (item name, category, description, rating) that can be easily replicated and shared across the network, preserving actionable information while enabling efficient exchange through standardized data formats
Solution Approach 2:
The patent introduces an intermediary recommendation system platform that mediates between users, capturing recommendations in structured format and distributing them through the social network, thereby preserving actionable answers while facilitating efficient information exchange
2Reliability
If detailed text descriptions are provided for recommendations, then context is preserved, but instant recall of specific items becomes challenging
Solution Approach 1:
The recommendation information is segmented into distinct structured fields (item_name, category, description, rating, recommendations) that can be independently accessed and recalled, allowing users to quickly retrieve specific item names while preserving full context in the structured record
Solution Approach 2:
The system uses visual indicators and structured formatting (analogous to color changes) to highlight key information such as item names, categories, and ratings, making specific items instantly recognizable and recallable while maintaining full contextual information in the structured data
3Adaptability or versatility
If existing social applications are used for information exchange, then communication can occur, but specific product recommendations are lost in full text responses
Solution Approach 1:
The system implements a universal structured recommendation framework that can accommodate multiple types of items (products, services, experiences) and recommendation contexts within a single standardized format, enabling both versatile communication and precise recommendation extraction through the same platform
Data Source
AI summary
A system that allows individuals to maintain lists of items of interest in an account, such as books, restaurants, hotels, clothes, etc. An individual can mark items in their account as recommended, after which other individuals in their sharing network can view the recommended items as grouped into categories. Items are displayed with action links (e.g. for purchasing, reservations, mapping, etc.) In an explore view categories are displayed for all of the combined recommended items from all of the individuals in the viewer's sharing network. When a category is selected from the explore view, an indication is provided for each item as to the individual who marked the item as recommended. An individual viewing a recommended item may also save the item to their own account as one to try later. The distribution of items between individuals through recommendations can be temporally and geographically mapped to identify patterns of interest.