History-Based Content Sharing Recommendations
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Solution Overview
Problem
Users of electronic devices face frustration and tedium when trying to share content items due to confusion over which sharing modality to use and the complexity of navigating menu-based selections.
Innovation Solution
An apparatus and method that tracks a user's content-sharing history to recommend suitable sharing actions based on past behaviors, such as sharing recipients, temporal data, and location, allowing for automatic or user-initiated display of recommended sharing options like email, social networks, or cloud services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple sharing modalities are provided, then sharing versatility is improved, but user confusion and operational complexity increase
Solution Approach 1:
The system automatically analyzes user behavior history and context to generate sharing recommendations without requiring users to manually navigate complex menus or make informed decisions about which sharing modality to use. The device serves itself by intelligently interpreting user intent and presenting optimized sharing options.
Solution Approach 2:
The system continuously monitors and analyzes user sharing behavior patterns, recency of sharing, content type, and contextual information to dynamically adjust and refine sharing recommendations. This feedback loop enables the system to learn from user interactions and improve recommendation accuracy over time.
2Adaptability or versatility
If multiple sharing modalities are provided, then sharing versatility is improved, but operational time and tedium increase
Solution Approach 1:
The system pre-analyzes user behavior history and maintains ready-to-present sharing recommendations based on patterns of recency, content type, and contextual factors. By preparing sharing options in advance based on historical data, the system eliminates the need for users to spend time navigating through multiple sharing modality options when the moment of sharing arises.
3Measurement precision
If sharing history tracking is implemented, then recommendation accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system extracts and focuses on specific key parameters from user sharing history such as recency of sharing, content type categories, and contextual information. Rather than processing all possible historical data, the system selectively extracts the most relevant features for generating accurate sharing recommendations, thereby managing complexity while maintaining precision.
Data Source
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AI summary
In one embodiment, an apparatus accesses a memory to obtain information regarding sharing history (as pertains, for example, to a particular user and/or device) and uses that sharing history to display at least one recommended sharing action (from amongst a plurality of available candidate sharing actions) as regards to a particular content item (such as, for example, a particular digital photograph, a video, a presentation, and so forth). This sharing history can comprise previously-selected sharing actions as correlated to content item types including shared-content recipients, corresponding temporal data, shared-content size, corresponding location data, and so forth. By one approach the apparatus itself serves to automatically track user-based content-sharing selections over time, which information is stored as the aforementioned sharing history. By one approach the user is presented with an opportunity to assert a nonspecific share command that triggers the aforementioned display of one or more recommended sharing actions.