Algorithmic Watchlist Sorting via Social Network Signals
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Solution Overview
Problem
Existing wishlists and watchlists fail to incorporate data from a user's social network connections, limiting their ability to update and refine items based on community activity, which hampers priority setting and new item discovery.
Innovation Solution
A computer-implemented system that creates an algorithmically sorted watchlist or wishlist, allowing users to create, rate, and recommend media or items, with updates based on user activity and aggregated community activity within a social network, including adding, removing, or reordering items based on social connections and community trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing wishlists and watchlists are maintained without incorporating social network data, then the system simplicity is preserved, but the ability to update and refine items based on community activity is limited
Solution Approach 1:
The patent introduces social network data as an intermediary layer between users and the wishlist/watchlist system. Social connections, community activity, and user interactions serve as mediators that automatically provide update signals without requiring direct user intervention. This resolves the contradiction by enabling dynamic adaptation through social intermediaries while maintaining relative system simplicity.
Solution Approach 2:
The system implements feedback loops where community activity (views, likes, shares, comments) continuously informs and updates the wishlist/watchlist rankings. This feedback mechanism enables automatic refinement of item priorities based on social signals, achieving high adaptability while the automated nature of the feedback reduces operational complexity.
2Productivity
If social network data is incorporated to dynamically update watchlists, then user engagement and discovery are enhanced, but data processing requirements increase
Solution Approach 1:
The patent extracts only the most relevant social signals (views, likes, shares, comments) from the broader social network data ecosystem. By selectively extracting specific engagement metrics rather than processing all available social data, the system enhances user engagement through social proof while managing data processing requirements through focused data extraction.
Solution Approach 2:
The system applies partial action by focusing on key social metrics that have the highest impact on user engagement rather than processing all possible social network data. This selective approach achieves effective productivity enhancement through the most influential social signals while avoiding the overhead of comprehensive data processing.
3Measurement precision
If algorithmic updates are performed frequently based on community activity, then content relevance is improved, but computational resources are consumed
Solution Approach 1:
The patent implements periodic algorithmic updates that recalculate watchlist rankings at scheduled intervals based on accumulated community activity. This periodic action maintains content relevance by regularly incorporating new social signals while optimizing computational resource usage by batching processing operations rather than continuously recalculating rankings in real-time.
Solution Approach 2:
The system performs preliminary data collection and aggregation of social signals before executing the full algorithmic ranking update. By preliminarily accumulating engagement data and pre-processing social signals, the system achieves high measurement precision in content relevance while reducing the computational burden during the actual ranking recalculation phase.
Data Source
AI summary
Described are platforms, systems, applications, and methods for algorithmically updating a user configured watchlist or wishlist based on activity of social network connections including: adding an item to a watchlist, consuming or acquiring an item, rating an item, recommending an item, and discussing an item, as well as aggregated activity of a community or population of users within the social network.