Message Log Prioritization via User Behavior Models
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Solution Overview
Problem
Existing methods for presenting message logs in instant messaging and social media applications often result in group conversations staying at the top of the list even if the user hasn't participated, and contacts or groups that were frequently interacted with in the past but not recently being moved to the bottom, causing inconvenience in conversation management.
Innovation Solution
A method and system that use user-specific models to determine listing priorities based on behavioral data, allowing for dynamic adjustment of conversation item positions in the list based on user interaction patterns, such as assigning lower weights to inactive group conversations and higher weights to recently engaged ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If message logs are sorted by time order, then the latest conversations are displayed at the top, but inactive group conversations remain at the top position causing clutter and user inconvenience
Solution Approach 1:
The patent changes the sorting parameter from simple time order to a composite relevance score that incorporates multiple factors including recency, user participation level, and message frequency. This allows the system to dynamically adjust conversation priorities based on actual user engagement patterns rather than just chronological order.
Solution Approach 2:
The system implements feedback mechanisms by monitoring user interaction patterns with conversation logs and using this information to adjust the display order. The relevance calculation incorporates user behavior data such as reading patterns, reply rates, and engagement frequency to continuously optimize which conversations are prioritized in the list.
2Productivity
If contacts or groups are sorted by recent activity, then frequently contacted items stay at the top, but previously frequent contacts that are now inactive are moved to the bottom causing user inconvenience
Solution Approach 1:
The system uses multiple parameters beyond simple recency, including historical interaction frequency, user-defined importance flags, and engagement patterns. This multi-dimensional approach allows previously frequent contacts to maintain higher visibility even during temporary inactivity periods, providing more flexible conversation management.
Solution Approach 2:
The conversation list order is made dynamic and adjustable rather than fixed by a single sorting rule. Users can modify weights and priorities of different parameters, and the system adapts the display order based on both automated relevance calculations and user preferences, allowing flexible management of conversation priorities.
Data Source
AI summary
A computing device with processor(s) and memory obtains user-specific models corresponding to a user of the computing device, where the user-specific models are configured to determine respective listing priorities for message logs based on a respective set of parameters generated based at least in part on previous behavioral data corresponding to the user. The computing device obtains a request from the user to display a listing of message logs with message logs of at least two distinct message types. In response to obtaining the request, the computing device: determines listing priorities for the message logs in the listing of message logs according to user-specific models corresponding to the least two distinct message types; determines a presentation order for the listing of message logs based on the determined listing priorities and a prioritization preference of the user; and presents the listing of message logs in the determined presentation order.