Streaming Menu Content Recommendation via User Behavior Clustering
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Solution Overview
Problem
Users face significant time and effort in navigating complex, multi-dimensional menu structures to find desired online content due to the vast amount of available content, and existing recommendation systems have varied usefulness.
Innovation Solution
Users are grouped into clusters based on common behavior patterns, with content recommendations generated based on their cluster membership, utilizing tracking data for content viewing and UI navigation behavior to customize the user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users navigate through complex multi-dimensional menu structures to find desired content, then they can locate specific content, but it takes considerable time and effort
Solution Approach 1:
The system performs preliminary actions by tracking user behavior patterns (viewing history, navigation paths, interaction sequences) and pre-computing user profiles and content recommendations before users need them. This allows the system to present personalized content suggestions proactively, eliminating the need for users to manually navigate through complex menus to discover relevant content.
Solution Approach 2:
The patent introduces an intermediary recommendation system that mediates between the vast content repository and the user. This intermediary analyzes user behavior data, matches users with similar preferences (user-based collaborative filtering), and presents filtered, personalized content recommendations. This intermediary layer shields users from the complexity of the full content menu structure while still enabling efficient content discovery.
2Reliability
If the system provides personalized content recommendations based on user behavior tracking, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The recommendation system is segmented into distinct functional modules: behavior tracking module (collecting user interactions), profile generation module (creating user representations), recommendation engine (generating suggestions), and feedback module (refining recommendations). This segmentation allows each component to be developed, optimized, and maintained independently, managing overall system complexity while achieving high recommendation accuracy through specialized processing in each module.
Solution Approach 2:
The system implements continuous feedback loops where user responses to recommendations (clicks, views, skips, ratings) are tracked and fed back into the profile generation and recommendation engines. This feedback mechanism progressively refines user profiles and improves recommendation accuracy over time. The feedback principle also enables the system to adapt to changing user preferences dynamically without requiring complete system redesign.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for recommending content based on user behavior tracking and analysis. Tracking data for a user indicating how the user interacted with a menu structure of a streaming media application is determined. A group from a plurality of user groups into which to group the user is selected based on the tracking data, wherein each group comprises data from a plurality of users. A plurality of content available for recommendation selection is determined. A first one of the plurality of content is selected as a content recommendation based on previous viewing patterns associated with the plurality of users of the selected group. The content recommendation is provided via the menu structure.