ML Help Intent Prediction for Streaming Support Interfaces
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Solution Overview
Problem
Existing digital content streaming platforms face inefficiencies in customer service solutions, leading to wasted computing resources and user frustration as users search for help without knowing how to describe their issues, resulting in fruitless self-help searches and lengthy wait times for live support.
Innovation Solution
A help intent prediction system that uses a machine learning model to analyze user navigation events and features to predict the type of help needed, dynamically customizing the help display with personalized solutions before the user initiates a query, thereby reducing resource waste and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users search through self-help articles and wait for live support, then they can access customer service channels, but computing resources are depleted and user experience deteriorates
Solution Approach 1:
The system performs preliminary action by predicting user help intent before the user actually seeks help. The machine learning model analyzes navigation events and user features to anticipate what help a user will need, allowing the system to prepare and present relevant self-service options proactively, thereby preventing resource depletion from fruitless searches and reducing wait times for live support.
Solution Approach 2:
The system enables self-service by dynamically generating personalized help displays that guide users to relevant self-service resources. Instead of requiring users to manually search through extensive help articles, the system uses ML predictions to automatically curate and present the most useful self-service options, allowing users to resolve issues independently without depleting computing resources on unnecessary searches.
2Loss of time
If users manually search for help solutions, then they can find information, but the process is time-consuming and frustrating
Solution Approach 1:
The system eliminates time loss by performing preliminary prediction of help intent before the user begins searching. The machine learning model processes navigation events and user features to predict what help the user needs, allowing the system to pre-generate and present personalized help displays that reduce or eliminate manual searching, thereby significantly decreasing help search time and improving user experience.
Solution Approach 2:
The system implements feedback by continuously monitoring user navigation events and using this information to refine help intent predictions. The machine learning model learns from user behavior patterns to improve its predictions over time, creating a feedback loop that increasingly accurate personalized help displays, thereby reducing search time and enhancing ease of operation.
3Adaptability or versatility
If the system provides generic help displays, then all users can access the same information, but personalization and relevance are reduced
Solution Approach 1:
The system applies local quality by customizing help displays to match individual user needs rather than providing uniform generic information. The machine learning model analyzes each user's navigation events and features to generate personalized help displays that contain only the most relevant information for that specific user, thereby eliminating irrelevant information while maintaining adaptability and versatility across different user types.
Solution Approach 2:
The system changes parameters by dynamically adjusting help display content based on predicted help intent and user features. The machine learning model modifies parameters such as recommended articles, support options, and information hierarchy to match individual user needs, thereby preventing loss of relevant help information while maintaining personalized adaptation for each user.
Data Source
AI summary
A computer-implemented method for predicting a user's help intent in relation to a digital streaming system and dynamically customizing a help display based on the predicted help intent. For example, embodiments discussed herein train a help intent machine learning model to generate help intent predictions based on various types of inputs. The embodiments discussed herein further leverage the generated help intent predictions to dynamically update a help display such that when a user lands on that display, predicted solutions that are customized to the user's most likely problem are immediately presented. Various other methods, systems, and computer-readable media are also disclosed.