User-Specific Recommendation via Historical Event Segmentation
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Solution Overview
Problem
Conventional recommendation systems for shared accounts, such as Netflix or smart devices, often fail to provide personalized recommendations to the currently active user as they are based on all past events from multiple users, leading to unsuitable suggestions.
Innovation Solution
The system compares current events to historical events to identify a subset likely generated by the same user, dividing historical event data into sessions and associating each session with a cluster representing a specific user, using methods like demographer, CutCost, or Mixture methods to accurately assign sessions to users, and then makes recommendations based on the cluster associated with the current user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If recommendation systems use all historical events from multiple users for shared accounts, then they can provide comprehensive recommendations based on abundant data, but they fail to provide personalized recommendations to the currently active user
Solution Approach 1:
The patent segments historical event data into distinct user-specific subsets by analyzing behavioral patterns, device information, location data, and temporal characteristics. This segmentation allows the system to separate mixed user activities into individual user profiles, enabling personalized recommendations while maintaining comprehensive data utilization. The system divides the aggregate historical data into user-specific clusters that can be independently analyzed for each active user.
2Productivity
If the system uses all past events indiscriminately for recommendations, then it maximizes data utilization, but it results in unsuitable suggestions for the current user
Solution Approach 1:
The patent applies local quality by tailoring the recommendation generation process to the specific characteristics of each user's behavioral patterns. Instead of applying a uniform recommendation approach to all users, the system analyzes local variations in user behavior including device preferences, time of day patterns, location contexts, and interaction styles. This allows the system to maintain high data utilization while ensuring each user receives recommendations suited to their specific local context and preferences.
3Adaptability or versatility
If the system attempts to identify the current user from mixed events, then it can improve personalization, but it increases system complexity
Solution Approach 1:
The patent implements preliminary action by pre-processing and organizing historical event data into user-specific clusters before recommendation generation. The system performs advance analysis of behavioral patterns, device information, and contextual data to segment and label user-specific event subsets in advance. This preliminary organization reduces the complexity of real-time user identification during active usage, as the system can quickly match current user characteristics against pre-segmented clusters rather than analyzing all raw historical data from scratch.
Data Source
AI summary
A method and a system of making a recommendation to a user that is currently generating events based on a subset of historical event data are provided. Historical event data, which is segmented into a set of sessions, is received. Each session includes events. The sessions are associated with clusters that represent the users that generated the historical event data. Each of the associated sessions is associated with one cluster and the number of the clusters is the same as the number of the users. A determination as to which cluster is associated with events currently being generated by a current user's behavior is made. The determining does not require identification of the current user. A recommendation is made to the current user based on the cluster that is associated with the events currently being generated.