Media Playlist Adjustment with Strong-Seed Recommendation Scoring

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Solution Overview

Problem

Existing media content applications struggle to effectively recommend and insert media content items into playlists based on user preferences and listening habits, leading to suboptimal user experience.

Innovation Solution

A machine learning model is used to predict scores for candidate media content items, selecting and inserting them into playlists based on their likelihood of being added by the user, with features and interactions being used to refine the model over time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a machine learning model is used to predict scores for candidate media content items, then the personalization and relevance of recommendations is improved, but the computational complexity and processing time increases

Engineering Contradiction:

Improvepersonalization of recommendationsVSAvoidcomputational complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system pre-computes and stores features for media content items and playlists in advance, so that when a recommendation is needed, the machine learning model can quickly score candidates using pre-prepared data rather than computing everything from scratch

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system focuses computational resources on scoring only the most promising candidate items that are locally relevant to the user's specific playlist and listening history, rather than uniformly processing all available media content

Inventive Principle:

Principle #3Local quality

2Measurement precision

If the system continuously learns from user interactions to refine recommendations, then the quality and accuracy of playlist recommendations is improved, but the data processing requirements and system resource consumption increases

Engineering Contradiction:

Improveaccuracy of recommendationsVSAvoiddata processing requirements

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The system implements feedback loops where user interactions with recommended items (plays, skips, additions to playlists) are continuously collected and used to retrain and refine the machine learning model, improving accuracy over time while processing only the necessary interaction data

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system processes a subset of user interactions and selectively updates the model with the most informative data points, rather than processing every single user action, reducing overall data processing requirements while maintaining improvement in recommendation quality

Inventive Principle:

Principle #16Partial or excessive action

3Ease of operation

If the system inserts recommended media content items into user playlists automatically, then the user experience and playlist quality is improved, but the risk of inserting irrelevant or unwanted content increases

Engineering Contradiction:

Improveuser experienceVSAvoidrelevance of inserted content

Core Design Contradiction:

Ease of operationVSObject-affected harmful factors

Solution Approach 1:

The system uses feedback from user responses to recommended items (whether they are played, skipped, or added to playlists) to continuously refine the scoring algorithm, ensuring that automatically inserted content becomes increasingly relevant and aligned with user preferences

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The machine learning model acts as an intermediary that translates user preferences and playlist characteristics into scored candidate recommendations, providing a buffer between user intent and actual content insertion to ensure relevance

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12430383B2Adjusting a playlist of media content items

Publication Date: 2025.09.30 SPOTIFY

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, the scores indicating a likelihood that a corresponding candidate media-content item will be added to the playlist, and inserting a candidate media-content item of the plurality of candidate media-content items after the strong seed media-content item based at least in part on the scores predicted for the plurality of candidate media-content items.