Real-Time Recommendation Engine for Personalized Offer Conflicts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing recommendation systems lack the ability to provide personalized and real-time offers, leading to potential negative impacts on user experience and suboptimal outcomes such as decreased sales and customer loyalty.
Innovation Solution
A method and system utilizing machine learning models to generate and deliver personalized item recommendations in real-time, based on user behavior data, offer strategies, and objective functions, incorporating features like stackable discounts and conflict resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional indiscriminate offer distribution is used, then coverage is broad, but relevance and user experience deteriorate
Solution Approach 1:
The patent segments the recommendation system into distinct functional modules: user behavior analysis module, offer generation module, real-time delivery module, and feedback processing module. Each module handles specific tasks independently, allowing the system to provide personalized offers without overwhelming complexity. The segmentation enables parallel processing of user data, offer rules, and delivery mechanisms.
Solution Approach 2:
The system performs preliminary actions by pre-processing user behavior data, pre-defining offer templates and rules, and pre-establishing delivery channels before actual recommendation needs arise. User profiles are pre-built with behavior patterns, and offer strategies are pre-configured based on business goals, enabling rapid real-time personalization without complex on-the-fly computations.
2Reliability
If real-time personalized recommendations are implemented, then user satisfaction improves, but processing time and computational resources increase
Solution Approach 1:
User behavior data is continuously pre-processed and stored in structured formats with pre-computed features and patterns. Offer templates and business rules are pre-configured and validated before deployment. This preliminary preparation enables the system to generate personalized recommendations in real-time by simply matching current user context against pre-processed data and templates, rather than computing everything from scratch.
Solution Approach 2:
The system uses template-based offer generation where standardized offer structures, discount rules, and presentation formats are pre-defined and copied/adapted for different users. Instead of creating unique offers from scratch for each user, the system copies proven offer templates and personalizes them with user-specific parameters, dramatically reducing processing time while maintaining personalization effectiveness.
3Adaptability or versatility
If extensive user data is collected for personalization, then recommendation relevance improves, but data privacy and security risks increase
Solution Approach 1:
The system extracts only the specific behavior patterns and preferences needed for offer personalization from extensive user data, rather than storing or processing all raw user information. Sensitive personal data is separated from behavioral data, with only anonymized or aggregated behavior patterns used for recommendation generation. This extraction approach enables effective personalization while minimizing privacy exposure.
Solution Approach 2:
The patent introduces an intermediary layer of behavior pattern analysis that stands between raw user data and the recommendation engine. Instead of directly using sensitive user information, the system processes data through intermediate representations such as behavior clusters, preference vectors, and engagement scores. This intermediary processing protects user privacy while preserving the personalization capability needed for effective recommendations.
4Productivity
If multiple offer strategies are applied simultaneously, then sales optimization improves, but conflict resolution complexity increases
Solution Approach 1:
The system applies different offer strategies to different user segments, product categories, or contextual situations rather than uniformly to all users. Each local context (user profile, item category, time of day, device type) receives appropriately tailored offer combinations. This local quality approach enables multiple strategies to coexist without conflict by ensuring they operate in distinct or complementary domains.
Solution Approach 2:
The patent merges multiple offer strategies into unified offer packages that combine different types of incentives (discounts, coupons, loyalty points, free shipping) into single coordinated recommendations. Instead of presenting separate competing offers, the system combines them into integrated proposals that work together synergistically, reducing conflict while maximizing sales potential through multi-faceted value propositions.
Data Source
AI summary
There are provided methods, systems and non-transitory storage mediums for providing personalized item recommendations to a user in real-time. Data related to items on a web resource and as user data comprising profiles, preferences, and past interactions with items of a plurality of users are received. Personalized strategies and constraints for a second set of items are received. An objective function representing optimization goals is also received. Using an inference engine, user behavior of a plurality users is inferred based on the received data. In real-time, user interactions of a user with the web resource is received, and personalized item recommendations are generated by a personalized recommendation engine for the user based on the user interaction, inferred user behavior, offer strategies, and the objective function. The personalized item recommendations, along with discount types and time periods, are transmitted to the user's device in real-time.