User-Tailored Recommendation System Using Segmented User Buckets
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Solution Overview
Problem
Conventional decision-support tools for choosing items like restaurants, bars, and movies fail to provide user-tailored recommendations, as they are designed for the mass market and do not account for individual users' unique needs, preferences, and traits.
Innovation Solution
A software and hardware facility that organizes users into groups or 'buckets' based on demographic information, personality types, preferences, and actions, using a decision tree structure to navigate users to relevant recommendations, adjusting ratings for confidence and tailoring suggestions to specific scenarios, and periodically reorganizing the tree to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional decision-support tools are designed for the mass market, then they can serve a broad audience, but they fail to provide user-tailored recommendations that account for individual preferences and traits
Solution Approach 1:
The patent segments users into distinct groups or 'buckets' based on demographic information, personality types, preferences, and actions. This segmentation allows the system to provide tailored recommendations to each user group while maintaining a manageable system structure. The decision tree structure further segments the user journey into manageable nodes and branches, making the complex task of personalization tractable.
Solution Approach 2:
The system changes parameters by dynamically adjusting ratings based on confidence levels and user-specific factors. Ratings are not static but are modified according to the user's bucket assignment, the reliability of the data, and scenario-specific adjustments. This allows the system to adapt recommendations to individual users without requiring a completely different system for each user.
2Measurement precision
If the system organizes users into many detailed groups or buckets, then recommendation accuracy improves, but the complexity of managing and navigating the decision tree increases
Solution Approach 1:
The decision tree is segmented into hierarchical levels, with broader categories at higher levels and more specific user buckets at lower levels. This hierarchical segmentation allows the system to achieve high prediction accuracy through detailed bucketing while managing complexity through the structured, multi-level organization. Users are guided through the tree step-by-step, making the complexity manageable.
Solution Approach 2:
The system performs preliminary actions by pre-organizing users into buckets and pre-calculating ratings for each bucket before users actually request recommendations. This preliminary organization and pre-computation of data reduces the complexity of real-time decision-making and allows for accurate, personalized recommendations without requiring complex runtime processing.
3Loss of information
If the system collects and processes extensive user information for personalization, then recommendation relevance improves, but the amount of data processing and storage requirements increase
Solution Approach 1:
The system merges multiple types of user information (demographic data, personality types, preferences, and actions) into a unified user profile and bucket assignment. By combining these diverse data sources into a single integrated framework, the system preserves comprehensive user preference information while managing data volume through consolidation and structured organization.
Solution Approach 2:
The system applies local quality by collecting different types and amounts of information tailored to each user's specific needs and characteristics. Not all users require the same level or type of data collection - the system adapts the depth and nature of information gathering to each user's context, preserving relevant preference information while minimizing unnecessary data collection.
Data Source
AI summary
A facility for producing an item recommendation for a selected user is described. The facility accesses an information resource that, for each of a number of buckets that each correspond to a different collection of personal information, identifies users for a members of the bucket into each of whom the entire collection of personal information applies. The facility selects a bucket among the plurality of buckets of which the selected user is a member. The facility accesses a number of item ratings that were each contributed by a member of the selected bucket other than the selected user. For each item rated among the accessed item ratings, the facility aggregates the ratings of the item. On the basis of items' aggregated ratings, the facility selects one or more rated items for recommendation to the selected user.