Ensemble Machine Learning for Skill Suggestion Ranking
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Solution Overview
Problem
Conventional skill suggestion services in online platforms fail to provide scalable and personalized recommendations for end-users to complete their profiles, as they rely on heuristic-based methods that are not effective in handling the vast variability in user characteristics and skills across millions of users.
Innovation Solution
An ensemble machine-learning technique is deployed, combining the outputs of heterogeneous models such as pair-wise decision trees and list-wise multilayer perceptron neural networks to rank and recommend skills, leveraging click tracking data and social signals to optimize the suggestion service, ensuring scalability and personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If heuristic-based methods are used for skill suggestion, then the system is simple to implement, but it fails to provide scalable and personalized recommendations for millions of users
Solution Approach 1:
The system segments the skill suggestion problem into multiple independent machine learning models, each handling different aspects of skill prediction. These models process user data independently and their outputs are combined to generate final recommendations, enabling personalized suggestions at scale without requiring a single monolithic complex system
Solution Approach 2:
The patent introduces an intermediary layer of machine learning models that mediate between raw user data and final skill recommendations. These models transform complex user behavior patterns into structured predictions, which are then combined to produce personalized skill suggestions, effectively decoupling data complexity from recommendation personalization
2Adaptability or versatility
If conventional skill suggestion services are used, then the implementation is straightforward, but they fail to handle the vast variability in user characteristics and skills
Solution Approach 1:
The system changes the parameters of the recommendation approach by using multiple machine learning models with different architectures and optimization objectives. Each model captures different aspects of user-skill relationships, and their combined outputs provide accurate recommendations that handle vast user variability through diversified parameter representations
Solution Approach 2:
The patent employs a composite approach by combining outputs from multiple heterogeneous machine learning models. This composite system leverages the strengths of different model types (e.g., collaborative filtering, content-based, hybrid models) to achieve high recommendation accuracy while handling diverse user characteristics that single models cannot capture
3Ease of operation
If profile information is made optional, then user registration is easier, but end-users are hesitant to add information to complete their profiles
Solution Approach 1:
The system implements self-service by automatically generating skill recommendations based on user behavior data without requiring manual input. The machine learning models analyze user interactions, preferences, and patterns to autonomously suggest relevant skills, allowing users to benefit from complete profiles without the effort of manually adding information
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously learns from user responses to skill recommendations. When users accept or reject suggested skills, this feedback is used to refine model predictions, creating a virtuous cycle that improves recommendation accuracy over time and encourages greater profile completion while maintaining registration ease
Data Source
AI summary
Techniques for ranking skills using an ensemble machine learning approach are described. The outputs of two heterogenous, machine-learned models are combined to rank a set of skills that may be possessed by an end-user of an online service. Some subset of the highest-ranking skills is then presented to the end-user with a recommendation that the skills be added to the end-user's profile. The ensemble learning technique involves a concept referred to as “boosting”, in which a weaker performing model is enhanced (e.g., “boosted”) by a stronger performing model, when ranking the set of skills. Accordingly, by using a combination of models, better results are achieved than might be with either one of the individual models alone. Furthermore, the approach is scalable in ways that cannot be achieved with heuristic-based approaches.