Personalized Search Suggestions for Feature Discovery in Applications
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Solution Overview
Problem
Users often fail to utilize the full range of features in applications due to difficulty in locating and accessing them, leading to inefficient use and reduced effectiveness.
Innovation Solution
A system that personalizes user interfaces by analyzing user and other users' histories to suggest frequently used and relevant features, organizing them into customizable sections like 'For You' and 'Suggested' for easy access, and providing personalized search suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If personalized recommendations are generated using traditional methods, then user experience can be improved, but the system complexity and computational resources required increase significantly
Solution Approach 1:
The patent replaces traditional complex collaborative filtering algorithms with a neural network-based recommendation system. The neural network learns user preferences and item characteristics automatically from data, substituting the mechanical rule-based matching and matrix factorization approaches with a more efficient deep learning model that reduces computational complexity while improving personalization accuracy.
Solution Approach 2:
The patent uses neural networks to create simplified representations (embeddings) of users and items that capture their essential characteristics. These compressed vector representations serve as copies of the complex user-item interaction patterns, enabling efficient recommendation generation without requiring the full complexity of the original data and algorithms.
2Adaptability or versatility
If traditional recommendation algorithms are used, then personalized content can be delivered, but the computational time and resources increase
Solution Approach 1:
The patent pre-trains the neural network model on large datasets of user interactions and item characteristics before deployment. This preliminary training phase captures general patterns and relationships in advance, so that during actual recommendation generation, the system only needs to perform efficient forward propagation through the trained network rather than computing complex algorithms in real-time.
Solution Approach 2:
The patent transforms the recommendation problem into a parameter optimization problem where the neural network learns optimal parameters (weights and biases) during training. Once trained, these fixed parameters enable rapid recommendation generation by simply computing the dot product of user and item embeddings, dramatically reducing computational time compared to iterative traditional algorithms.
3Measurement precision
If more user data is collected for better personalization, then recommendation accuracy improves, but privacy concerns and data security risks increase
Solution Approach 1:
The patent extracts only the essential features and patterns from raw user data through the neural network's feature extraction layers. Instead of storing or processing all raw user data including sensitive information, the system extracts only the necessary preference patterns and behavioral signals needed for recommendations, leaving behind unnecessary personal details that could pose privacy risks.
Solution Approach 2:
The neural network acts as an intermediary between raw user data and recommendation outputs. It processes user data through multiple transformation layers, converting sensitive raw data into anonymized embeddings and preference vectors that preserve recommendation accuracy while removing personally identifiable information and reducing privacy risks.
Data Source
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AI summary
A method and system for providing a personalized user interface and personalized search suggestions when using an application is disclosed. The method includes receiving a request for providing a personalized user interface such as a personalized menu for an application and examining a history of the user's usage of the application, a history of other user's usage of the application, or content of the current document in the application, before identifying suggested options offered by the application based on one or more of these factors. A list may then be generated and provided for being displayed as part of the personalized user interface. Upon receiving an indication of a user's intent to perform a search within the application, the user's usage history, usage history of users with similar activities, and/or content of the current document may be examined to provide the user with a list of search suggestions.