Personalizing Content via Social Affinity Graphs
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Solution Overview
Problem
Existing content delivery systems fail to personalize content effectively for users based on their social affinity graphs, leading to a lack of relevance and engagement.
Innovation Solution
A computer system identifies contextually-related content items associated with users in a social affinity graph and alters content items to include these personalized elements, selecting them based on metrics such as relationship closeness and allocated space, to increase user appeal and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content items are personalized by including contextually-related content from social affinity graphs, then user engagement and relevance are improved, but system complexity and processing requirements increase
Solution Approach 1:
The system segments the content personalization process into distinct modules: identifying the user's social affinity graph, selecting contextually-related content items based on metrics, determining allocation space, and inserting the personalized content. This modular approach manages complexity by handling each aspect separately rather than as a monolithic system.
Solution Approach 2:
The system performs preliminary actions by pre-identifying the user's social affinity graph and pre-selecting contextually-related content items based on metrics such as relationship closeness and content quality. This preparation is done before the actual content delivery, reducing real-time processing complexity while maintaining personalization effectiveness.
2Measurement precision
If contextually-related content items are selected based on multiple metrics including relationship closeness and allocated space, then content relevance is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selecting content items based on a subset of available metrics rather than all possible metrics. It prioritizes key metrics such as relationship closeness and allocated space, while potentially using other metrics like content quality or user preferences as secondary factors. This selective approach maintains precision while reducing computational overhead.
3Ease of operation
If content items are altered to include personalized elements from users in the social affinity graph, then user appeal and interaction are increased, but content delivery system complexity increases
Solution Approach 1:
The system introduces an intermediary content management layer that handles the complexity of personalization. This intermediary component receives standard content items, automatically identifies appropriate personalized elements from the user's social affinity graph, and generates the final personalized content. This mediator shields the core content delivery system from personalization complexity while enabling enhanced user interaction.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for personalizing content items. One of the methods includes identifying, by a computer system, a content item to be provided to a client device of a first user. The method includes determining that the content item is capable of including a contextually-related content item associated with a second user, the second user having a relationship of a first user in a social affinity graph. The method includes altering, by the computer system, the content item to include the contextually-related content item. The method includes providing the content item to the client device.