Playlist Adjustment Using Strong-Seed Media Item Scoring
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Solution Overview
Problem
Existing media content applications lack effective methods for dynamically adjusting playlists based on user preferences and interactions, leading to suboptimal playback experiences.
Innovation Solution
A machine learning model is employed to predict scores for candidate media content items, allowing for their insertion into playlists based on likelihood of user addition, with features and interactions used to refine the model over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a machine learning model is used to predict scores for candidate media content items, then playlist customization and user engagement are improved, but device complexity and computational resources increase
Solution Approach 1:
The system divides the playlist adjustment process into distinct components: a machine learning model for score prediction, a feature compilation module for extracting playlist characteristics, and an insertion module for adding candidate items. This segmentation allows each component to be optimized independently while managing overall system complexity.
Solution Approach 2:
The machine learning model pre-calculates scores for multiple candidate media content items based on playlist features before user interaction. This preliminary scoring enables rapid recommendation generation when users request playlist adjustments, avoiding real-time computational complexity during user sessions.
2Productivity
If real-time prediction and insertion of candidate media content items is implemented, then user engagement and playlist relevance are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs score predictions for candidate media content items in advance, storing these scores for rapid retrieval during user interactions. This eliminates the need for real-time computation during playlist adjustment requests, significantly reducing processing time while maintaining recommendation quality.
Solution Approach 2:
The system focuses computational resources on predicting scores only for candidate items that are most likely to be inserted based on playlist features and strong seed identification. By concentrating predictions on relevant candidates rather than all possible items, processing time is reduced while maintaining high user engagement through personalized recommendations.
Data Source
AI summary
This disclosure is directed to adjusting a playlist of media-content items. One aspect is a method comprising receiving a request to adjust a playlist comprising initial media-content items, in response to receiving the input requesting the playlist be adjusted, compiling a set of features for the playlist and selecting a strong seed media-content item from the initial media-content items as a strong seed, predicting scores for a plurality of candidate media-content items based at least in part on the set of features for the playlist and the strong seed, and interleaving a candidate media-content item of the plurality of candidate media-content items based at least in part on the scores predicted for the plurality of candidate media-content items.