Compact Profile Generation for Scalable Recommendation Systems
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Solution Overview
Problem
Existing recommendation systems face limitations in scalability, dependency on user ratings, restricted cross-category recommendations, and invasive data collection, failing to effectively match user preferences across diverse and changing product catalogs.
Innovation Solution
A system and method that generates compact, universal profiles for both users and objects based on behavioral interactions, allowing for scalable prediction of affinities and recommendations without requiring explicit demographics or expert knowledge, enabling cross-category comparisons and temporal responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If collaborative filtering is used to generate recommendations based on user ratings, then recommendation accuracy is improved, but device complexity and data storage requirements increase linearly with the number of rated objects
Solution Approach 1:
The patent segments the recommendation problem into two independent components: user-based collaborative filtering and item-based collaborative filtering. This segmentation allows the system to store user-item interaction matrices separately, enabling efficient computation and reducing overall system complexity while maintaining recommendation accuracy.
Solution Approach 2:
The patent introduces a dual-dimensional approach by simultaneously applying collaborative filtering from both user perspective and item perspective. This dimensional transformation enables the system to handle sparse data more effectively and reduces computational complexity by distributing the calculation burden across two independent filtering mechanisms.
2Measurement precision
If detailed user profiles and explicit feedback are collected to improve recommendation personalization, then recommendation accuracy is improved, but loss of information increases due to invasive data collection requirements
Solution Approach 1:
The patent implements self-service by allowing users to implicitly provide feedback through their natural interactions with the system (views, purchases, searches) without requiring explicit profile creation or demographic information. The system automatically generates recommendations based on this implicit behavioral data, eliminating the need for invasive data collection while maintaining personalization accuracy.
Solution Approach 2:
The patent replaces the traditional mechanical approach of explicitly collecting user demographic and preference data with an automated system that infers user preferences from behavioral patterns. This substitution eliminates the need for invasive questionnaires and profile creation, protecting user privacy while still enabling accurate recommendations.
3Measurement precision
If mentor-based collaborative filtering is used to make recommendations, then recommendation quality is improved for users with mentors, but productivity decreases due to lengthy questionnaires and limited scalability
Solution Approach 1:
The patent creates a universal recommendation system that functions effectively for all users regardless of whether they have mentors or not. By implementing both user-based and item-based collaborative filtering, the system provides quality recommendations to new users (who lack mentors) and established users alike, eliminating the need for lengthy onboarding questionnaires and enabling immediate scalability.
Solution Approach 2:
The patent performs preliminary action by pre-computing item similarity matrices and user profiles based on implicit behavioral data before users need recommendations. This allows the system to immediately generate quality recommendations for new users without requiring them to complete lengthy questionnaires, thereby improving both recommendation quality and system scalability from the first interaction.
Data Source
AI summary
A system and method is disclosed for profiling a subject's search engine keywords and results based on relevancy feedback. Because the system is based on the search behavior of the user, the profiling is language independent and balances the specificity of search terms against the profiled interests of the user. The system can also score keywords on their search effectiveness and eliminate ineffective keywords from the keyword index. The system can also synthesize new keyword combinations to assist the user in refining the search or acquiring related content. The system has application in text mining, personalization, behavioral search, search engine optimization, and content acquisition, to name but a few applications.