Channel Preference Scoring for Sparse User Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing collaborative recommendation techniques for audio/video (A/V) content struggle to provide effective recommendations when users rarely switch between channels, leading to poor user engagement and content provider challenges in retaining users with diverse viewing habits.
Innovation Solution
A system that groups users based on their channel watching and browsing behaviors, generating channel preference scores by analyzing probabilities of channel switching and behavioral similarity, allowing for targeted content recommendations and promotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If collaborative recommendation techniques are used to recommend A/V content based on user ratings, then content recommendations can be provided, but poor recommendations are generated when few users switch between channels
Solution Approach 1:
The patent changes the parameters used for recommendation from simple collaborative filtering based on ratings to a multi-factor model that incorporates channel switching probabilities, browsing behavior features, and user segment similarities. This parameter transformation enables reliable recommendations even when direct user-to-user switching data is scarce.
Solution Approach 2:
The patent introduces user segments as an intermediary layer between individual users and recommendation generation. By grouping users into segments based on browsing behavior and channel preferences, the system can leverage aggregate segment-level patterns to generate recommendations when individual user data is insufficient.
2Adaptability or versatility
If user behavior data is analyzed to generate channel preference scores, then targeted recommendations can be provided, but system complexity increases
Solution Approach 1:
The patent segments users into distinct groups based on browsing behavior and channel preferences. This segmentation simplifies the complexity by allowing the system to process and analyze user cohorts collectively rather than handling each individual user's complete behavior pattern separately, making the overall system more manageable.
Solution Approach 2:
The patent adds a new dimension to the recommendation system by incorporating browsing behavior features and channel switching probabilities alongside traditional rating data. This multi-dimensional approach enables targeted recommendations while distributing the computational complexity across multiple independent calculation layers.
Data Source
AI summary
Multiple channels of audio/video (A/V) content in a system are available to users. A channel scoring system uses a user's channel watching behavior and channel browsing behavior to generate a channel preference score for various different channels in the system. The channel watching behavior refers to which channels the user watches. The channel browsing behavior refers to features describing the user's behavior when watching A/V content provided by a channel. Examples of these features include the hour of the day when the user watches the A/V content, the number and length of A/V content provided by the channel watched in a session, device preferences of the user, and so forth. Various actions can be taken based on the generated channel preference scores, such as recommending or otherwise promoting channels to the user.