Session Clustering for Media Personalization
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Solution Overview
Problem
Media content providers face challenges in personalizing content effectively due to their inability to identify patterns in user interaction that account for both context and listening behavior, where similar behaviors can have different meanings in various contexts.
Innovation Solution
Implementing a method that uses a machine-learning algorithm to sort user sessions into clusters based on session characteristics, allowing for personalized content delivery by associating user interaction with these clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional user data tracking methods are used to understand user preferences, then content personalization can be provided, but the ability to identify patterns of user interaction that account for both context and listening behavior is insufficient
Solution Approach 1:
The patent segments user sessions into distinct clusters based on multiple characteristics including context, listening behavior, and interaction patterns. By dividing the user data space into meaningful segments (clusters), the system can identify patterns more accurately without requiring a single monolithic complex model, thus improving measurement precision while managing complexity through modular clustering approaches.
Solution Approach 2:
The patent introduces additional dimensions for analyzing user behavior by considering multiple session characteristics simultaneously (context, listening behavior, interaction patterns). This multi-dimensional approach allows the system to capture nuanced patterns that single-dimensional conventional methods miss, improving accuracy by viewing user behavior from multiple angles rather than increasing complexity in a single direction.
2Measurement precision
If user sessions are sorted into multiple groups and clusters using machine-learning algorithms, then content personalization accuracy is improved, but processing complexity and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing user sessions into hierarchical groups and sub-clusters. This multi-level segmentation allows the machine-learning system to process data in manageable segments rather than attempting to analyze all user behavior simultaneously, improving personalization accuracy through detailed clustering while reducing overall computational complexity through divide-and-conquer processing.
Solution Approach 2:
The patent implements partial action by initially sorting sessions into broader groups before applying more refined clustering algorithms to specific subsets. This staged approach applies complex machine-learning processing only where needed rather than uniformly to all data, improving personalization accuracy in critical areas while reducing unnecessary computational complexity in less critical segments.
3Measurement precision
If sessions are sorted into groups using sorting rules and then divided into clusters, then user interaction patterns are better identified, but processing time and computational resources are consumed
Solution Approach 1:
The patent applies preliminary action by first sorting sessions into groups using relatively simple sorting rules before applying more complex clustering algorithms. This preliminary organization of data into logical groups reduces the scope of subsequent clustering operations, improving classification accuracy through systematic preprocessing while reducing overall processing time by avoiding redundant computations on unorganized data.
Solution Approach 2:
The patent segments the processing task into distinct phases: initial sorting into groups followed by clustering within groups. This segmentation allows each phase to be optimized independently - the sorting phase uses efficient rule-based methods for quick initial organization, while the clustering phase applies more computationally intensive algorithms only to smaller, pre-organized subsets, thereby improving accuracy while managing processing time.
Data Source
AI summary
A server system divides respective groups of a plurality of groups of sessions for multiple users of the media-providing service into respective pluralities of clusters. The server system tracks user interaction with a client device during a user session and identifies the user session as belonging to a first cluster of the pluralities of clusters based at least in part on the user interaction. The server system personalizes content for the user session using one or more content criteria associated with the first cluster, in accordance with identifying that the user session belongs to the first cluster.