Controllable Diffusion Gallery Recommendations for Dynamic Preference Capture
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Solution Overview
Problem
Existing image gallery recommendation systems have limitations in capturing the full dynamics of user preferences due to limited user interaction with the underlying algorithms, primarily relying on static image engagement methods like liking/disliking and commenting.
Innovation Solution
Implementing a controllable diffusion model with an interactive widget in an image gallery recommendation service, allowing users to dynamically guide image generation through various inputs such as text, sketches, and graphical selections, enabling iterative refinement of generated images to meet user-specific preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional image gallery recommendation systems use static interaction methods (liking/disliking, commenting), then the system structure remains simple, but the ability to capture full dynamics of user preferences is limited
Solution Approach 1:
The patent transforms static image interaction into dynamic interaction by introducing a controllable diffusion model that generates images in real-time based on user inputs. The system evolves from displaying fixed gallery images to dynamically generating and refining images through iterative user feedback, allowing the recommendation system to adapt continuously to user preferences rather than relying on predetermined static content
Solution Approach 2:
The controllable diffusion model serves as an intermediary between the user and the image gallery system. Instead of users directly interacting with static images through simple like/dislike buttons, their inputs (text, sketches, graphical selections) are mediated through the diffusion model which translates these into refined image generations, adding a layer of intelligent transformation that enhances preference capture capability
2Measurement precision
If users are given direct control over algorithm parameters, then user preference accuracy improves, but ease of operation decreases
Solution Approach 1:
The system enables users to directly guide the image generation process through multiple interaction modalities (text input, sketching, graphical selections on the generated image). Users serve themselves by providing feedback that the diffusion model processes to refine subsequent image generations, giving them direct control over the recommendation outcome without requiring them to understand underlying algorithmic parameters
Solution Approach 2:
The controllable diffusion model accepts various types of user inputs (text prompts, sketch inputs, graphical selections marking regions to keep or remove) that effectively change the parameters guiding image generation. These parameter changes are translated from simple user actions into complex generation controls, allowing precise preference expression through intuitive interfaces rather than direct parameter manipulation
3Adaptability or versatility
If the system uses only static gallery images, then device complexity is low, but the ability to provide personalized recommendations is limited
Solution Approach 1:
The system transitions from displaying static pre-selected gallery images to dynamically generating images on-the-fly using a controllable diffusion model. Each user interaction triggers a new generation cycle where images are created and refined in real-time based on user feedback, transforming the recommendation system from a static catalog to a dynamic generation engine that adapts to individual user preferences
Solution Approach 2:
The diffusion model performs preliminary image generation based on initial user inputs before presenting options to the user. This preliminary action creates a starting point for iterative refinement, where the system proactively generates images that approximate user preferences and then allows users to guide further refinement, reducing the need for users to search through extensive static galleries
Data Source
AI summary
Aspects of the disclosure include methods and systems for leveraging a controllable diffusion model for dynamic image search in an image gallery recommendation service. An exemplary method can include displaying an image gallery having a plurality of gallery images and a dynamic image frame. The dynamic image frame can include a generated image and an interactive widget. The method can include receiving a user input in the interactive widget and generating, responsive to receiving the user input, an updated generated image by inputting, into a controllable diffusion model, the user input. The method can include replacing the generated image in the dynamic image frame with the updated generated image.