Bi-Directional Recommendation Structure for Fast Personalized Offers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Businesses in industries with high customer interaction sensitivity, such as insurance, face negative customer perceptions due to factors like high service costs or industry reputation, leading to low customer engagement and potential loss of customers to competitors.
Innovation Solution
Implementing recommender systems that generate efficient iterative electronic recommendation structures to offer personalized discounts, track user savings, and push offers to customers, fostering brand loyalty by maintaining a high level of customer interaction through periodic updates and location-specific promotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional recommendation systems are used to provide personalized offers, then customer engagement and brand loyalty are improved, but storage space requirements and system complexity increase
Solution Approach 1:
The patent segments the recommendation system into distinct modules: ratings vector aggregation module, similarity pairing generation module, and offer recommendation module. Each module performs a specific function, allowing the system to be more manageable and easier to operate while maintaining personalized recommendations for customer engagement.
Solution Approach 2:
The patent introduces an intermediary data structure (similarity pairing values derived from ratings vectors) that mediates between raw user ratings and final offer recommendations. This intermediary layer simplifies the operation by pre-processing and organizing data in a way that makes recommendation generation more efficient and manageable.
2Quantity of substance
If comprehensive offer catalogs are made available to customers, then customer benefits and brand loyalty increase, but the time and effort required for customers to search through offers increases
Solution Approach 1:
The patent performs preliminary action by pre-aggregating ratings vectors and pre-generating similarity pairings between offers and user preferences. This pre-processing allows the system to quickly retrieve and recommend relevant offers without requiring customers to search through comprehensive catalogs, thus maintaining offer availability while reducing search time.
Solution Approach 2:
The patent implements feedback mechanisms where user interactions with offers (clicks, selections, purchases) are continuously fed back into the ratings vectors. This feedback loop enables the system to learn and adapt, improving recommendation accuracy over time and reducing the time needed for customers to find relevant offers in the comprehensive catalog.
3Ease of operation
If personalized recommendations are generated in real-time, then customer experience and engagement improve, but computational resources and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-computing ratings vector aggregations and similarity pairings during off-peak periods or in batch processes. This allows real-time recommendation generation to rely on pre-processed data, significantly reducing computational resource requirements while maintaining personalized customer experience.
Solution Approach 2:
The patent implements a dynamic recommendation system where the level of personalization and real-time processing adapts based on system load and user context. For common query patterns, pre-computed recommendations are served; for novel or complex scenarios, real-time computation is performed, optimizing the balance between customer experience and computational resource usage.
Data Source
AI summary
Systems and methods are described for providing user offers based on efficient iterative recommendation structures. In various aspects, a server invokes a bi-directional look-up interface via a lookup request, where the bi-directional look-up interface is exposed via an electronic recommendation structure. The lookup request causes the bi-directional look-up interface to return a bi-directional recommendation value. The bi-directional recommendation value indicates a likelihood of a first user selecting a first offer or a second offer. The bi-directional recommendation value is transmitted via a computer network to a client device associated with the first user upon a determination that the likelihood meets or exceeds a recommendation threshold. The client device is operative to display at least one of the first offer or the second offer.