Dynamic User Vector Clustering for Personalized Content Recommendations
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Solution Overview
Problem
Current media recommendation methods are imperfect, often failing to provide content of interest to users, leading to disillusionment with media services.
Innovation Solution
An apparatus and system that determine user vectors, cluster users, update vectors based on events, and recommend content based on cluster associations, using machine learning and collaborative filtering to predict user responses and output personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation methods are used, then the system is simple to implement, but the recommendation accuracy and user interest match deteriorates
Solution Approach 1:
The patent segments users into distinct clusters based on their behavior patterns and preferences. By dividing the user base into segments (clusters with different engagement levels and content preferences), the system can provide tailored recommendations for each segment, significantly improving recommendation accuracy while managing complexity through structured segmentation.
Solution Approach 2:
The patent dynamically changes system parameters including user vectors that represent user preferences, cluster assignments that group users by behavior, and recommendation weights that prioritize different content types. These parameter changes enable the system to adapt to evolving user preferences and improve recommendation precision over time.
2Adaptability or versatility
If static user profiles are used, then the system is computationally efficient, but the adaptability to user behavior changes deteriorates
Solution Approach 1:
The patent implements dynamic user profiles through continuously updated user vectors that reflect current behavior patterns. Instead of static profiles, the system maintains evolving representations of user preferences that adapt in real-time based on new interactions, ensuring high adaptability to behavior changes while using efficient vector operations.
Solution Approach 2:
The system incorporates feedback loops where user interactions with recommended content are continuously monitored and used to update user vectors and cluster assignments. This feedback mechanism enables the system to learn from user responses and adapt its recommendations, improving adaptability while maintaining efficiency through incremental updates rather than complete reprocessing.
3Reliability
If frequent vector updates are performed, then the recommendation relevance improves, but the computational load and processing time increases
Solution Approach 1:
The patent implements periodic updates of user vectors and cluster assignments rather than continuous real-time updates. By updating these parameters at scheduled intervals based on accumulated user interactions, the system maintains recommendation relevance while avoiding the excessive computational load of continuous processing, thus reducing time loss.
Solution Approach 2:
The system performs preliminary clustering and vector calculations in advance based on available data, preparing user segments and preference profiles before they are needed for recommendations. This preliminary action allows the system to have updated, relevant recommendations ready without incurring high processing costs at the moment of recommendation delivery.
Data Source
AI summary
There is described an apparatus for providing a recommendation to a first user, wherein the apparatus comprises: a processor arranged to: determine, at a first time, a first set of vectors for a plurality of users, including a vector associated with the first user; determine a set of clusters based on the first set of vectors; detect, at a second time, an event associated with the first user; determine an updated vector for the first user in dependence on the event; place the first user into a cluster of the set of clusters based on the updated vector; and determine a recommended item of content for the first user in dependence on the cluster; and a user interface and/or communication interface arranged to: output the recommendation and/or the recommended item to the first user.