Collaborative Message Relevance Filtering via Word-User Association
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Solution Overview
Problem
In unified collaboration systems, users face the challenge of sifting through large volumes of messages to identify relevant information, as only a small percentage of messages are pertinent to their specific needs, leading to information overload and distraction.
Innovation Solution
A system and method that utilize analytics to associate words in messages with users based on their relevance, allowing the server to notify relevant users and filter out non-relevant information by updating a table of word-user associations, thereby alerting users to messages that require attention and hiding irrelevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all messages in a collaborative system are delivered to all users, then complete information availability is achieved, but information overload and user distraction increase significantly
Solution Approach 1:
The system extracts and identifies only the relevant subset of messages for each user based on word-user associations. By applying analytics to determine relevance, the system separates pertinent information from non-pertinent information, delivering only the extracted relevant messages to each user rather than all messages.
Solution Approach 2:
The system applies different message delivery quality to different users based on their individual word-user associations. Each user receives a customized subset of messages tailored to their specific interests and roles, rather than a uniform delivery to all users. This localizes the information quality to match each user's needs.
2Object-affected harmful factors
If analytics are applied to determine message relevance, then user distraction is reduced, but system complexity increases
Solution Approach 1:
The system performs preliminary word-user association analysis in advance, building a table of associations between words and users before messages need to be filtered. This pre-computed association data is stored and reused for rapid message relevance determination, avoiding the need to perform complex analytics in real-time when messages are delivered.
Solution Approach 2:
The system introduces an intermediary analytics component that sits between message reception and message delivery. This intermediary applies the pre-computed word-user associations to filter messages, acting as a mediator that simplifies the overall system architecture by centralizing the filtering logic in a dedicated component rather than distributing complexity throughout the system.
Data Source
AI summary
A system and method are presented for detecting messages relevant to users in a collaborative environment. In a unified collaboration system, large volumes of messages between a plurality of users in a group may be monitored for relevance to a particular user. Analytics may be applied to the content of the messages to determine which of the plurality of users are relevant and should be alerted. Alerts may notify relevant users that there are messages which may require attention. Non-relevant information in messages may also be hidden or filtered for a user. In an embodiment, users and subject matters may be linked together. For example, words in a message may be related to specific sub-topics of a group and may be associated with a user over time based on when the word is used and which users respond.