Deep Learning Recommendation Engine for Personalized App Suggestions
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Solution Overview
Problem
Current recommendation systems fail to provide personalized third-party application recommendations to users, often due to limited data usage and lack of consideration for individual user attributes, leading to poor user engagement and a negative perception of the application and its implementing organization.
Innovation Solution
A recommendation engine utilizing two deep-learning models to generate personalized recommendations by processing user data and application data, including time-dependent and static profile information, to determine relevance and usage likelihood, thereby providing tailored third-party application suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic recommendations are provided to all users, then implementation complexity is reduced, but user engagement and recommendation quality deteriorate
Solution Approach 1:
The patent segments the recommendation system into multiple components: user profile analysis module, application data processing module, matching algorithm module, and feedback module. Each module handles specific tasks independently, allowing the system to process personalized recommendations without overwhelming complexity. The segmentation enables modular development and maintenance while delivering customized recommendations to each user.
Solution Approach 2:
The patent introduces multiple dimensions for recommendation generation: user attributes (preferences, behavior patterns, demographics), application attributes (category, features, popularity), and contextual dimensions (time, device, usage scenario). By analyzing recommendations across these multiple dimensions simultaneously, the system achieves high personalization quality without requiring exponential complexity growth.
2Device complexity
If limited data sources are used, then data processing complexity is reduced, but recommendation personalization deteriorates
Solution Approach 1:
The patent implements a universal data processing framework that can handle multiple data sources and formats through a single standardized interface. The system ingests structured data (databases), semi-structured data (logs, JSON), and unstructured data (text, images) using the same processing pipeline, enabling comprehensive personalization without requiring separate complex processing systems for each data type.
Solution Approach 2:
The patent introduces data preprocessing and normalization layers that act as intermediaries between diverse data sources and the recommendation engine. These intermediary modules standardize data formats, clean and validate inputs, and transform various data types into a unified representation that the core recommendation algorithm can process efficiently, reducing overall system complexity.
3Device complexity
If unstructured data is not processed, then processing complexity is reduced, but available recommendation data deteriorates
Solution Approach 1:
The patent replaces manual or rule-based extraction methods with automated machine learning models for processing unstructured data. Natural language processing models extract insights from user reviews and descriptions, computer vision models analyze images and videos, and neural networks process text data. This substitution automates the extraction process, reducing manual complexity while maximizing information recovery from unstructured sources.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for generating a recommendation of third-party applications to a user by a recommendation engine. The recommendation engine includes two deep-learning models that use various data sources (e.g., user data and application data) to generate the recommendation. One deep-learning model generates a relevance score for each available third-party application. The relevance score is used to determine a relevant application(s). The other deep-learning model generates a connection score for each relevant application. The recommendation engine uses the relevance score and the connections to generate an engagement score for each relevant application to determine whether the user would use the third-party application if recommended to the user. Those relevant applications with an engagement score that meet pre-determined criteria are determined and displayed to the user in the application as a recommendation.