Video Delivery System Action Feed Generation
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Solution Overview
Problem
Traditional TV broadcast systems require users to manually manage schedules and track content, leading to missed opportunities for watching specific episodes or content featuring favorite actors, due to lack of awareness or availability issues.
Innovation Solution
A video delivery system generates and ranks actions for entities, such as TV shows, movies, and actors, using real-time user context to provide personalized action feeds, including watch, follow, and try actions, integrating on-demand and live content options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually manage TV schedules and track content, then they can control when to watch, but they miss content due to lack of awareness or availability issues
Solution Approach 1:
The system automatically generates and delivers personalized content recommendations to users without requiring manual schedule management. The recommendation engine analyzes user preferences and broadcast schedules to proactively inform users about available content, eliminating the need for users to manually track schedules while preventing information loss about content availability.
2Ease of operation
If users manually program DVR to record content, then they can watch content at convenient times, but they still need to know the schedule in advance
Solution Approach 1:
The system performs preliminary analysis of broadcast schedules and user preferences to generate recommendations before users need to watch content. By pre-calculating optimal viewing times and content availability based on historical data and user profiles, the system eliminates the need for users to manually research schedules in advance, saving time while enabling convenient viewing.
3Adaptability or versatility
If users watch entire TV shows to find favorite actors, then they can discover content, but they may not watch content they are not interested in
Solution Approach 1:
The system analyzes individual user preferences and historical viewing behavior to generate highly personalized content recommendations. Instead of requiring users to watch entire shows to discover favorite actors, the system identifies specific content segments and recommendations tailored to each user's interests, allowing users to efficiently access only the content they are likely to enjoy based on their unique viewing patterns.
Data Source
AI summary
In one embodiment, a method generates actions for entities found on a video delivery system based on information for user behavior of a user on the video delivery system and generates probabilities for the actions for the entities based on the actions for the entities and the user behavior. A probability for an action indicates the probability the user would select that action for an entity when compared against other actions in the set of actions for the set of entities. The method then selects an action feed based on the probabilities for the set of actions. The action feed includes at least a portion of the actions for the entities. The action feed is outputted to the client for display on an interface where an action on an entity in the action feed is performed by the video delivery system when selected by the user on the interface.