Automated Multi-Image Post Generation via Image Categorization
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Solution Overview
Problem
Social networking systems face challenges in generating effective multi-image posts, as third-party content providers find it time-consuming to manually create them and may not be aware of the potential for automated generation, leading to reduced engagement and increased computing load.
Innovation Solution
The social networking system automatically generates multi-image posts by accessing external servers, analyzing images and information, categorizing them, and selecting relevant images for display, reducing the need for extensive input from third parties and decreasing computing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If third-party content providers manually create multi-image posts, then the posts can be customized and published, but it is time-consuming and increases computing load
Solution Approach 1:
The system performs preliminary actions by automatically gathering images from external sources (websites, APIs, databases) and pre-processing them before the posting moment. The social-networking system proactively creates the multi-image post content in advance, eliminating the need for third-party providers to manually assemble images at the time of posting.
Solution Approach 2:
The system enables self-service by allowing third-party content providers to simply provide a URL or trigger keyword, while the social-networking system automatically handles the entire multi-image post creation process including image gathering, selection, and assembly. The system serves itself by using its own resources and capabilities to generate the post content without requiring manual intervention from external providers.
2Ease of operation
If third-party content providers manually create multi-image posts, then they have control over content, but it increases computing load on the social-networking system
Solution Approach 1:
The system extracts the computationally intensive image gathering, selection, and assembly operations from the third-party provider's device and relocates them to the social-networking system's infrastructure. By moving these resource-intensive tasks to the platform's servers, the system reduces the computing burden on external devices while maintaining centralized control over the post generation process.
Solution Approach 2:
The social-networking system acts as an intermediary between the third-party content provider and the final post publication. The provider simply inputs a URL or keyword, and the system mediates the entire complex process of fetching images from external sources, selecting appropriate ones, and assembling the multi-image post, thereby simplifying the provider's task while managing the computational complexity centrally.
3Productivity
If single-image posts are used, then the posting process is simple, but engagement and click-through rates are lower
Solution Approach 1:
The system merges multiple individual images into a single cohesive multi-image post structure. By combining several relevant images gathered from external sources into one unified post containing multiple images, the system enhances user engagement and visual appeal while maintaining a streamlined posting process for third-party providers.
Solution Approach 2:
The system creates a universal multi-image post format that can serve multiple purposes and engage users more effectively. The generated posts can be used across different contexts and platforms, providing multi-functionality while automatically optimizing image selection and arrangement to maximize engagement without requiring providers to understand complex post structure design.
Data Source
AI summary
In one embodiment, a method includes accessing, by a crawling module, a first structured document from an external server, where the first structured document is associated with a first post by a third-party content provider and includes multiple image objects and information associated with the image objects, extracting from the first structured document a sub-set of image objects and information associated with each of the image objects, analyzing the extracted image objects and information to identify categories of image objects based on their features, selecting a first category of image objects that has a highest number of image objects compared to a number of image objects in each other category of image objects, and generating a second post including a multi-image display that includes two or more of the image objects from the first category of image objects.