Hybrid Promotion Recommendation System Using Lifecycle-Aware Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating promotion recommendations are insufficient due to limitations in addressing contextually relevant promotions, particularly with limited promotion lifetimes and varying stages of the promotion lifecycle, leading to ineffective recommendation systems.
Innovation Solution
A hybrid method combining modified collaborative filtering and static similarity, leveraging promotion characteristics such as multilevel taxonomy, freshness, and proximity, along with real-time optimization to enhance recommendation accuracy and relevance, including co-purchase signals and free shipping qualifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional collaborative filtering is used for promotion recommendations, then user-based similarity can be captured, but it fails to account for limited promotion lifetimes and varying stages of the promotion lifecycle
Solution Approach 1:
The patent applies dynamics by making the recommendation system adaptive to different promotion lifecycle stages. The system dynamically adjusts its behavior based on whether a promotion is in introduction, growth, maturity, or decline stage, allowing the recommendation engine to evolve its strategy according to the temporal context of each promotion rather than using a static approach.
Solution Approach 2:
The patent changes key parameters of the collaborative filtering algorithm to account for promotion lifetimes. It modifies the similarity calculation to incorporate time-decay factors and promotion-age weights, transforming the standard algorithm into one that can handle the temporal constraints of limited-duration promotions and varying lifecycle stages.
2Reliability
If a hybrid method combining modified collaborative filtering and static similarity is used, then recommendation relevance is improved, but system complexity increases
Solution Approach 1:
The patent merges two distinct recommendation approaches: modified collaborative filtering (which captures dynamic user-promotion interactions) and static similarity based on promotion characteristics (which provides consistent baseline recommendations). By combining these methods into a hybrid system, the patent achieves more robust and relevant recommendations that leverage both user behavior patterns and inherent promotion attributes.
Solution Approach 2:
The patent introduces an intermediary layer that bridges the modified collaborative filtering component and the static similarity component. This intermediary processes outputs from both methods, reconciles their results, and generates the final recommendation set, allowing the system to benefit from both approaches while managing their integration systematically.
3Productivity
If real-time optimization is implemented to promote free shipping bonuses, then consumer engagement increases, but computational resources and processing time are consumed
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing promotion characteristics, similarity metrics, and free shipping threshold requirements. This pre-processing allows the real-time optimization component to quickly retrieve and apply pre-computed data rather than calculating everything from scratch, significantly reducing the computational energy required during live recommendation generation.
Solution Approach 2:
The patent applies partial action by implementing real-time optimization selectively for promotions that meet certain criteria (e.g., those close to free shipping thresholds or with high engagement potential). Rather than optimizing all recommendations in real-time, the system focuses computational resources on the most impactful cases, achieving good consumer engagement while conserving energy.
Data Source
AI summary
In general, embodiments of the present invention provide systems, methods and computer readable media for recommending contextually relevant promotions to consumers in order to facilitate their discovery of promotions that they are likely to purchase from a promotion and marketing service.