Multitask Deep Learning for Cross-Pillar Next-Action Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning systems struggle to provide personalized and cross-pillar holistic recommendations for user actions in online networks, lacking scalability and coherence across different platforms and contexts.
Innovation Solution
A multitask deep machine learning model is employed to analyze user interactions and intent, providing personalized and contextual recommendations by integrating data from various online network pillars, optimizing multiple objectives, and ensuring coherence with current contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing machine learning systems are used for user action recommendations, then implementation is straightforward, but the system cannot provide personalized and cross-pillar holistic recommendations
Solution Approach 1:
The patent implements a universal recommendation framework that integrates multiple data sources including user profiles, social connections, content interactions, and contextual information into a single machine learning model. This multi-functional system processes diverse input types (structured and unstructured data) to generate personalized recommendations across different online network contexts, achieving both personalization and cross-pillar holistic recommendations without requiring separate systems for each data type
2Reliability
If data from various online network pillars is integrated, then recommendation coherence improves, but processing complexity increases
Solution Approach 1:
The patent merges multiple data sources and processing functions into a unified machine learning model architecture. The system combines user profile data, social connection data, content interaction data, and contextual data into integrated feature vectors that are processed by a single model producing coherent recommendations. This consolidation reduces the complexity of managing separate processing pipelines while maintaining recommendation coherence across different data pillars
3Adaptability or versatility
If the system is made highly adaptable to different network surfaces, then versatility improves, but customization effort increases
Solution Approach 1:
The patent creates a universal recommendation engine that adapts to different network surfaces through a standardized interface layer. The machine learning model processes data from various sources (social networks, content platforms, communication tools) using the same architecture and algorithms, requiring minimal customization when deploying to new platforms. The system achieves cross-platform versatility through configurable data sources and context parameters rather than platform-specific implementations
Data Source
AI summary
In an example embodiment, machine learning is utilized to make recommendations for next actions by users of an online network. These next actions are called “next best actions.” The machine learning may be performed to train a multitask deep machine learning model to make recommendations based on a series of inputs, including, for example, contextual information that relies upon action sequences of the user and historical users, and user intent. The use of a multitask deep machine learning model allows for the model to generate action recommendations that are personalized, contextual, and coordinate across various different aspects of the online network, rather than being limited to only a single aspect. Likewise, the multi-task deep machine learning model can also be tailored to optimized different use-case specific objectives while at the same time being easy to scale and maintain.