Social Network Question Routing via User Profile Modeling
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Solution Overview
Problem
Users face challenges in identifying the most suitable friends within their social network to ask questions, as public broadcasting can be intrusive and time-consuming, while direct messaging may miss relevant and timely responses due to lack of knowledge or availability insights.
Innovation Solution
A recommendation engine processes user messages to determine subjects, locations, and active times, creating models to predict user willingness, availability, and knowledge, ranking friends for question responses based on these factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a user publicly broadcasts a question to the entire network, then the user can reach a broader audience and increase the chances of getting a good response, but the user may irritate friends when done too often or for topics that do not interest most people
Solution Approach 1:
The patent segments the audience by creating topic-specific interest groups rather than broadcasting to the entire network. Users are divided into segments based on their expressed interests and knowledge levels, allowing targeted question distribution to relevant segments only, thereby increasing response rate while avoiding friend irritation from irrelevant broadcasts
Solution Approach 2:
The patent applies local quality by customizing the question distribution strategy for different user segments. Instead of uniform broadcasting, the system identifies local characteristics of each segment (interests, knowledge levels) and tailors the approach accordingly, sending questions only to segments with relevant interests and appropriate knowledge levels
2Productivity
If a user publicly broadcasts a question to the entire network, then the user can reach a broader audience, but the undirected request may easily be lost amongst many other postings
Solution Approach 1:
The patent segments the information flow by creating dedicated channels for different topic areas. Questions are routed to specific interest groups rather than being mixed in a general feed, ensuring that questions remain visible and accessible to the relevant audience without being lost amongst unrelated postings
Solution Approach 2:
The patent introduces an intermediary system (the interest group matching mechanism) that acts as a mediator between the questioner and potential responders. This intermediary filters and routes questions to appropriate segments, ensuring visibility and preventing information loss in the crowded general feed
3Object-affected harmful factors
If a user directly messages friends to ask questions, then the user avoids irritating friends and losing questions, but it is time-consuming to think of who to engage with
Solution Approach 1:
The patent enables self-service by having the system automatically identify and match users based on their expressed interests and knowledge levels. Users don't need to manually think about who to message; the system serves itself by analyzing user profiles and automatically routing questions to appropriate segments, saving time while maintaining targeted delivery
Solution Approach 2:
The patent performs preliminary action by pre-classifying users into interest groups and knowledge levels before questions are asked. This pre-organization of user information allows the system to quickly match questions with appropriate responders without requiring real-time manual selection, reducing time consumption
4Object-affected harmful factors
If a user directly messages friends to ask questions, then the user avoids irritating friends, but users may not have good insight into who is knowledgeable about a topic
Solution Approach 1:
The patent implements feedback mechanisms where users continuously update their interest preferences and knowledge levels, which are then used to refine the matching algorithm. This feedback loop ensures that the system's assessment of user knowledge and interests becomes increasingly accurate over time, improving measurement precision while maintaining automated targeted delivery
Data Source
AI summary
Messages generated by user accounts in a social networking application over a period of time are processed to determine the subjects and topic associated with the messages, as well as the geographical locations of the users associated with the user accounts, and the times when the users associated with the accounts are most active. The determined, subject, time, and location information is used to create a model that may be used to predict whether a user in the social networking application is willing, available, and has the knowledge or topical affinity to answer a question proposed by another user in the social networking application based on a subject, time, and/or location associated with the question. When a user enters a question, the user may be presented with a list of their friends or contacts ranked according to the probabilities generated by the model.