Automated Content Exploration via Intent Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional streaming platforms fail to provide personalized content recommendations based on users' current and historical interactions, leading to user frustration and wasted time in finding desired content.
Innovation Solution
Implementing a content exploration feature that uses machine learning algorithms to infer user intent through click stream data, ranking content based on viewing probability, and presenting curated trailers to guide users to relevant content in an automated exploration mode.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional streaming platforms provide static or generic recommendations, then the system complexity is low, but the user satisfaction and content discovery efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user interaction data (clickstream data, viewing history, search queries) before generating recommendations. Machine learning models are pre-trained on this data to infer user intent and preferences, enabling personalized recommendations without requiring complex real-time processing during user interactions.
Solution Approach 2:
The patent introduces an intermediary layer consisting of machine learning algorithms and intent inference systems between the user and the content library. This intermediary analyzes user behavior patterns and translates them into personalized content recommendations, resolving the contradiction by adding intelligence without directly increasing system complexity in the user-facing interface.
2Productivity
If users manually browse through large variety of content choices, then the content selection comprehensiveness is high, but the time consumption and user frustration increase
Solution Approach 1:
The system implements feedback loops by continuously monitoring user interactions with recommended content and adjusting future recommendations accordingly. Clickstream data, viewing completion rates, and engagement metrics are fed back into the machine learning models to refine intent inference and improve content discovery efficiency over time, reducing the time users spend browsing.
Solution Approach 2:
The patent changes key parameters of the recommendation system by dynamically adjusting content weighting, recommendation algorithms, and presentation formats based on inferred user intent. Instead of static recommendations, the system modifies recommendation parameters in real-time based on user behavior, significantly improving content discovery efficiency while minimizing browsing time.
3Ease of operation
If generic recommendations are provided to all users, then the system operation simplicity is maintained, but the relevance and personalization of content suggestions deteriorate
Solution Approach 1:
The recommendation system operates autonomously by automatically collecting user interaction data, inferring user intent through machine learning, and generating personalized recommendations without requiring explicit user input or configuration. This self-service approach maintains ease of operation while preventing loss of user preference information, as the system independently captures and utilizes behavioral data to personalize content suggestions.
Data Source
AI summary
Techniques for generating an automated guide for exploring content of a content streaming platform based on inferred intent are described herein. For example, click stream data from interactions with a content streaming platform during a session, along with historical click stream data of historical interactions with the content streaming platform during historical sessions may be obtained. A score for each piece of content from a plurality of content offered by the content streaming platform may be determined based on the click stream data and the historical click stream data. The plurality of content may be ranked based on the associated scores for each piece of content. Media content may be generated that includes a portion of an associated trailer from a subset of the ranked plurality of content based on the score for each piece of content of the subset. The media content may be presented via a user interface.