Topic Graph Object Recommendation for Social Networking Relevance
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Solution Overview
Problem
Conventional social networking systems struggle to recommend relevant objects to users as the number of objects increases, often suggesting unrelated objects, leading to decreased user interaction.
Innovation Solution
The system selects a seed object connected to the user, extracts topics from it, and uses a topic graph to identify related topics and users, generating scores for candidate objects based on user connections and topic relatedness to recommend more relevant objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional social networking systems identify objects connected to users who are also connected to an object connected to the user, then objects are identified to the user, but the identified objects may be unrelated to objects that are connected to the user, decreasing the likelihood of user interaction
Solution Approach 1:
The system performs preliminary extraction of topics from seed objects connected to the user before generating recommendations. By pre-processing the seed objects to identify their topics and using these topics to filter and score candidate objects, the system ensures that recommended objects are relevant to the user's interests without requiring complex real-time analysis during the recommendation generation phase.
Solution Approach 2:
Topics serve as an intermediary between user connections and recommended objects. Instead of directly connecting users to objects based on user-user connections, the system uses topics extracted from seed objects as a mediating layer. This intermediary mechanism ensures that recommended objects are related to the user's existing connections through shared topics, improving relevance while maintaining system manageability.
2Quantity of substance
If the social networking system maintains an increasing number of objects, then more content is available to users, but users have difficulty identifying objects likely to be of interest
Solution Approach 1:
The system uses feedback from user connections to seed objects and topic relationships to guide recommendations. By analyzing which objects users are already connected to and what topics those objects represent, the system generates feedback signals that inform the selection and scoring of candidate objects. This feedback mechanism helps users navigate the increasing number of objects by prioritizing those most likely to match their interests.
Solution Approach 2:
The system changes the parameter of object selection from purely connection-based to topic-based scoring. By introducing topic relevance as a scoring parameter and combining it with connection strength, the system transforms how objects are presented to users. This parameter change makes it easier for users to identify interesting objects among the increasing number of available objects by highlighting those with high topic relevance to their existing connections.
Data Source
AI summary
A social networking system recommends pages or other objects to a user with which the user may establish a connection to receive content associated with an object. Candidate objects may be identified by the social networking system as objects connected to additional users who are connected to an object connected to the user. To recommend objects with which the user is likely to have an interest, the social networking system extracts one or more topics from the object connected to the user and from various candidate objects. Based on a topic graph, the social networking system determines measures of relatedness between topics extracted from various candidate objects and an object connected to the user. The measures of relatedness are then used to select one or more of the candidate objects to identify to the user.