Delayed In-Situ Recommendation Engine for Store Traffic
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Solution Overview
Problem
Conventional recommendation systems face challenges in accuracy, efficiency, and flexibility when generating digital recommendations for in-store viewing, particularly due to their inability to effectively analyze physical items and user context, leading to inaccurate and resource-intensive suggestions.
Innovation Solution
A delayed in-situ recommendation system that utilizes a collaborative filter recommendation engine to process item categorization, physical store traffic modeling, historical return analysis, and inventory data to generate accurate and context-aware recommendations for in-store viewing, allowing users to accept or decline suggested items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation systems generate digital suggestions for in-store viewing, then item recommendations are provided to client devices, but the accuracy of recommendations deteriorates due to inability to effectively analyze physical items and user context
Solution Approach 1:
The system segments the recommendation generation process into multiple specialized components: a collaborative filter recommendation engine that processes item categorization data, physical store traffic modeling data, historical return analysis data, and inventory data separately, then integrates them to generate accurate in-store viewing recommendations
Solution Approach 2:
The patent introduces an intermediary recommendation system that bridges the gap between conventional digital recommendation systems and physical store contexts. This intermediary system translates digital item data into context-aware physical store recommendations by incorporating store traffic patterns, historical returns, and inventory availability
2Productivity
If conventional recommendation systems generate item suggestions, then digital recommendations are provided, but computing resource consumption increases due to resource-intensive processing
Solution Approach 1:
The system performs preliminary actions by pre-processing and categorizing item data before recommendation generation. Item categorization, physical store traffic modeling, and historical return analysis are conducted in advance, allowing the recommendation engine to quickly retrieve and combine pre-processed data without intensive real-time computation
Solution Approach 2:
The patent changes the parameters of recommendation generation by shifting from real-time complex computation to utilizing pre-computed metrics such as item categorization scores, traffic modeling results, and historical return rates. This parameter transformation reduces computing resource requirements while maintaining recommendation quality
3Adaptability or versatility
If conventional recommendation systems provide generic suggestions, then item recommendations are generated, but flexibility deteriorates in implementing recommendations for delayed in situ viewing
Solution Approach 1:
The system implements dynamic recommendation generation that adapts to delayed in-situ viewing scenarios. The recommendation engine adjusts its output based on timing parameters, allowing recommendations to be generated in advance for later store viewing while maintaining relevance through continuous data integration
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and providing recommendations to view items in store by providing item categorization, physical store traffic modelling, historical analysis of returns, and inventory data to a delayed in-situ collaborative filter recommendation engine. In particular, in one or more embodiments, the disclosed systems receive selection of an item to purchase online and pick up in store from a client device. In response, in one or more embodiments, the disclosed systems determine item categorization, accesses physical store traffic modelling, and/or generates an analysis of historical return of items. Further, in one or more embodiments, the disclosed systems utilize a delayed in-situ collaborative filter recommendation engine to determine a recommendation of an additional item to view in store.