Social Graph Entity Recommendation System
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Solution Overview
Problem
Users face difficulties in finding relevant businesses or entities in a geographic area due to unstructured and unreliable information on the internet, making it hard to determine entities of interest.
Innovation Solution
A computer-implemented method that utilizes a user's social graph to recommend entities by obtaining their geographic location, contacts, and associations, displaying these recommendations with association information such as reviews and ratings, and ranking them based on relationship criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If general internet information sources are used to provide entity information, then the quantity of information is increased, but the reliability and trustworthiness of the information deteriorates
Solution Approach 1:
The patent introduces social graph contacts as an intermediary layer between the user and entity information. Instead of directly presenting unverified internet information, the system uses trusted contacts to endorse and recommend entities, thereby maintaining information quantity while significantly improving reliability through social verification.
2Quantity of substance
If unstructured information is presented to users, then the quantity of entities is increased, but the ease of determining interesting entities deteriorates
Solution Approach 1:
The patent applies local quality by differentiating the presentation of entities based on their association with social graph contacts. Entities endorsed by trusted contacts are highlighted and prioritized in the display, allowing users to quickly identify interesting entities without being overwhelmed by the total quantity of available entities.
3Device complexity
If traditional search methods are used, then the device complexity is minimized, but the productivity of finding relevant entities deteriorates
Solution Approach 1:
The system performs preliminary action by pre-computing and storing associations between users, their social graph contacts, and entities in advance. When a user searches for entities, the system can quickly retrieve and filter pre-processed association data, significantly improving productivity without adding substantial complexity to the search interface or user interaction model.
Data Source
AI summary
Provided are methods and computer-readable media for providing recommended entities based on a user's external social graph, such as asymmetric social graph of a social networking service. In some embodiments, entities responsive to a search query or other request may be obtained. Each entity may be evaluated to determine if the entity is associated with a contact from a user's social graph. The association may include an evaluation (e.g., a rating, review, other evaluation or combination thereof) of the entity by the contact. Additionally, the contacts having associations with an entity may be ranked based on a relationship score with a user. The entities having associations with the contacts from a user's social graph may be provided as recommended entities to the user, and the association may be annotated to the recommended entity for viewing by the user.