New User Content Recommendations Using Genre Preference Clusters
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Solution Overview
Problem
Conventional content recommendation systems struggle to provide accurate recommendations for new users due to the lack of user history, leading to insufficient content onboarding pages.
Innovation Solution
A machine learning-based approach that clusters content preferences of existing users to select and populate onboarding pages for new users, using models like EM, k-means, or k-NN to determine content genres and recommend content based on popular groupings, even without prior user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation systems use user history to generate content recommendations, then recommendation accuracy is improved for existing users, but new users receive no or inaccurate recommendations due to lack of user history
Solution Approach 1:
The system pre-generates content recommendations for new users by clustering content preferences from existing users before the new user actually interacts with the system. This preliminary action ensures that new users receive accurate recommendations immediately upon joining, eliminating the cold start problem without waiting for user history to accumulate.
Solution Approach 2:
The system creates synthetic user profiles for new users by copying and adapting preference patterns from similar existing users through clustering. Instead of requiring new users to build their own preference history, the system replicates proven preference patterns from the user base, allowing new users to receive recommendations as if they had existing history.
2Measurement precision
If content recommendation systems wait for user interaction history to accumulate, then recommendation accuracy improves, but new user onboarding pages remain empty or partially populated
Solution Approach 1:
The system performs preliminary content selection and populates recommendation pages before new users complete their onboarding process. By pre-generating recommendations based on clustered user preferences, the system ensures that onboarding pages are fully populated with relevant content immediately, rather than waiting for user interactions to accumulate.
Solution Approach 2:
The system generates a complete set of content recommendations for new users even though the new user has no interaction history. This excessive action of providing full recommendations without requiring minimum user history ensures that onboarding pages are fully populated, and the system later refines these recommendations as user preferences become known.
3Adaptability or versatility
If conventional systems rely on extrapolating recommendations from past content, then recommendations are tailored to user interests, but new users with no past content receive no recommendations
Solution Approach 1:
The system introduces clustered user preference patterns as an intermediary between the recommendation engine and new users. These clusters serve as a mediator that translates the preferences of existing users into actionable recommendations for new users, bridging the information gap without requiring direct user history from the new user themselves.
Solution Approach 2:
The system changes the parameter basis for recommendations from individual user history to aggregated user preference clusters. By transforming the recommendation basis from user-specific data to population-level patterns, the system enables personalized recommendations for new users based on statistical preferences rather than individual history.
Data Source
AI summary
Systems and methods for generating and presenting content recommendations to new users during or immediately after the onboarding process, before any history of the new user's viewed content is available. A machine learning or other model may be trained to determine clusters of content genre values corresponding to genres of content watched by viewers. Clusters are thus associated with popular groupings of content genres viewed by many users. Clusters representing popular groupings of content genres may be selected for new users, and content corresponding to the selected clusters may be recommended to the new users as part of their onboarding process. A sufficient amount of content may be selected to fully populate any content recommendation portion of a new user onboarding page.