Video Pushing Method for TV Box User Experience
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users experience poor video selection options on TV boxes, requiring repeated browsing to find desired content, which negatively impacts user experience.
Innovation Solution
A video pushing method that determines target videos based on user page display frequency, using popularity of videos when frequency is low and user feature information when frequency is high, to continuously provide relevant content to the user terminal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the TV box displays limited network resources on the homepage, then the system complexity is reduced, but the user experience deteriorates as users need to repeatedly browse to find desired videos
Solution Approach 1:
The server performs preliminary actions by analyzing user behavior data in advance and pushing recommended videos to the user terminal before the user actively searches for them. This includes collecting user click streams, play histories, and search queries, then pre-computing and delivering personalized video recommendations to the terminal's local storage, so users can access recommended content without browsing the homepage repeatedly.
Solution Approach 2:
The user terminal performs self-service by locally storing and managing recommended video content without requiring continuous server interaction. The terminal maintains a local database of pushed videos, automatically updates recommendations based on user interactions, and provides instant access to personalized content, reducing the need for users to manually browse through limited homepage resources.
2Loss of information
If the server pushes videos based on user feature information, then the content relevance is improved, but the computational complexity increases
Solution Approach 1:
The video recommendation system is segmented into distinct functional modules: a user behavior analysis module that processes user data, a video matching module that compares user profiles with video features, and a push delivery module that transmits recommendations. This segmentation allows each module to specialize in specific computational tasks, improving efficiency and making the overall system more manageable despite the complexity of analyzing user features and video content.
Data Source
AI summary
Provided are a video pushing method, an apparatus, a device, and a storage medium. In an embodiment of the present disclosure, the number of times a user page is displayed on a user terminal is obtained; if the number of times is less than a preset value, a target video to be pushed to the user terminal is determined according to a popularity of a video in a storage system corresponding to a server; if the number of times is greater than a preset value, a target video to be pushed to the user terminal is determined according to feature information of a user and feature information of a video in the storage system corresponding to the server. Accordingly, the server can continuously push videos that meet the user's needs to the user terminal while the user is using the user page, thereby improving the user experience.