Mood-Dependent Ranked List Generation for User Preference Segmentation
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Solution Overview
Problem
Top-selling/rated lists fail to capture the subtleties of user preferences as they rely on a single ranking that does not account for individual variations in user tastes and moods, leading to inadequate representation of user preferences, especially when insufficient individual-level data is available.
Innovation Solution
A system and method for generating mood-dependent top selling/rated lists by learning a sparse distribution from partial preferences, processing this distribution to rank items, and producing multiple ranked lists that reflect different user types or moods, using pair-wise comparisons and marginal data to create a non-parametric distribution that captures the preferences of various user populations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single top-selling/rated list is used to reflect population preferences, then the system is simple and intuitive, but it fails to capture the subtleties of user preferences and cater to non-typical users
Solution Approach 1:
The patent segments the population into multiple user types or moods based on their preferences, creating multiple ranked lists instead of a single list. Each list caters to a specific user segment, allowing the system to capture diverse preferences while maintaining simplicity through aggregate-level data.
Solution Approach 2:
The system dynamically selects which ranked list to present based on the user's current mood or preference type. This dynamic adaptation allows the recommendation system to respond to varying user needs without requiring complex individual-level data collection.
2Measurement precision
If personalized recommendation engines are used to capture individual user preferences, then the accuracy of preference representation improves, but the device complexity and data requirements increase significantly
Solution Approach 1:
The patent introduces an intermediary approach by using mood-dependent ranked lists as a middle ground between simple aggregate lists and complex personalized recommendations. This intermediary solution captures individual preferences through mood-based segmentation without requiring extensive individual-level data or complex recommendation algorithms.
Solution Approach 2:
The system changes the parameter of user representation from individual-level detailed profiles to mood-based categorical segments. This parameter change allows for more accurate preference representation while reducing system complexity and data requirements.
3Measurement precision
If personalized recommendations are implemented, then user preference accuracy improves, but the loss of time and resources for data collection and processing increases
Solution Approach 1:
The patent performs preliminary action by pre-segmenting the population into mood-based user types and creating ranked lists for each segment in advance. This allows the system to provide accurate personalized recommendations without requiring real-time data collection or complex processing when a user makes a query.
Data Source
AI summary
A system and method for determining a rank aggregation from a series of partial preferences is presented. A distribution is learned over preferences from partial preferences with sparse support. A computer receives a plurality of partial preferences selected from two or more preference lists. Weights are assigned to each of said plurality of partial preferences, resulting in multiple ranked lists.