Context-Aware Add-In Deep Link Recommendation Using Embeddings
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Solution Overview
Problem
Users face challenges in finding and utilizing relevant add-in deep links for specific tasks due to the abundance of available add-ins, unawareness of add-in capabilities, and context-dependent relevance, leading to inefficiencies in accessing and using add-in functionality.
Innovation Solution
An add-in recommendation system utilizing a generative AI model to analyze user context, generate context embeddings, and compare them to add-in embeddings to recommend relevant deep links based on similarity, integrated with a client application to display these recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search through many add-ins to find relevant deep links, then they can access add-in functionality, but the time and effort required increases significantly
Solution Approach 1:
The system automatically generates add-in recommendations by analyzing user context, document content, and usage patterns without requiring manual search. The recommendation engine self-services by proactively identifying and presenting relevant add-in deep links based on embedded features and contextual analysis.
Solution Approach 2:
The patent replaces manual searching mechanics with an automated AI-based recommendation system. Instead of users manually browsing add-ins, the system uses machine learning models to analyze context and automatically generate personalized recommendations, substituting mechanical user effort with intelligent automation.
2Adaptability or versatility
If users are provided with all available add-in deep links, then they have complete access to functionality, but the complexity of the interface and information overload increases
Solution Approach 1:
The system applies local quality by providing customized add-in recommendations specific to each user's context, document type, and usage patterns. Instead of presenting all add-ins uniformly, the interface adapts to show only locally relevant recommendations based on the current task and user needs.
Solution Approach 2:
The patent segments the large set of available add-ins into smaller, context-relevant subsets. The recommendation engine divides add-ins based on functional categories, usage patterns, and contextual relevance, presenting only the segmented subset that applies to the current user situation.
3Reliability
If the system provides context-aware add-in recommendations, then relevance and user experience improve, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and embedding add-in metadata, document features, and user context information before recommendation generation. This advance preparation reduces the computational burden during actual recommendation queries, as the heavy lifting of feature extraction and embedding is done beforehand.
Solution Approach 2:
The patent employs parameter changes by adjusting the complexity and depth of contextual analysis based on system state, user preferences, and resource availability. The recommendation engine can dynamically modify parameters such as embedding dimensionality, analysis depth, and filtering criteria to balance relevance with computational efficiency.
Data Source
AI summary
An add-in recommendation system receives add-in recommendation requests from an add-in recommendation client which includes context information pertaining to user interactions with a client application. The context information is mapped to an embedding space using an encoder to generate a context embedding. An add-in recommendation engine compares the context embedding to an add-in index using to identify a predetermined number of add-in deep links to include in an add-in recommendation. The add-in index includes a plurality of add-in embeddings, each add-in embedding being associated with an add-in deep link which is accessible to the user and being used to map a semantic description of the add-in deep link to the embedding space. The add-in recommendation to the add-in recommendation client and displayed in a user interface of the client application.