GENERATING ITEM RECOMMENDATIONS UTILIZING A DELAYED IN-SITU RECOMMENDATION ENGINE

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GENERATING ITEM RECOMMENDATIONS UTILIZING A DELAYED IN-SITU RECOMMENDATION ENGINE - A simplified explanation of the abstract

This abstract first appeared for US patent application 18183023 titled 'GENERATING ITEM RECOMMENDATIONS UTILIZING A DELAYED IN-SITU RECOMMENDATION ENGINE

The present disclosure involves systems, computer-readable media, and methods for providing recommendations on items to view in a store using item categorization, store traffic modeling, historical return analysis, and inventory data.

Systems receive selection of an item for online purchase and in-store pickup, then determine item categorization, access store traffic modeling, and analyze historical return data.
Utilizes a delayed in-situ collaborative filter recommendation engine to suggest additional items to view in-store based on the user's selection.

Potential Applications: - Retail industry for enhancing the in-store shopping experience. - E-commerce platforms looking to bridge the online and offline shopping experiences.

Problems Solved: - Providing personalized recommendations for in-store viewing based on online purchases. - Improving customer satisfaction and engagement in physical retail locations.

Benefits: - Increased sales through targeted in-store recommendations. - Enhanced customer experience by offering relevant suggestions based on online behavior.

Commercial Applications: Title: Enhanced In-Store Shopping Recommendations Technology This technology can be utilized by retail chains, department stores, and online retailers to improve the in-store shopping experience and drive sales through personalized recommendations.

Prior Art: Readers can explore prior art related to collaborative filtering recommendation systems, item categorization algorithms, and store traffic modeling techniques.

Frequently Updated Research: Stay updated on advancements in collaborative filtering algorithms, customer behavior analysis, and retail technology integration.

Questions about the Technology: 1. How does the delayed in-situ collaborative filter recommendation engine work in providing in-store recommendations? 2. What are the key factors considered in generating personalized suggestions for in-store viewing?

Original Abstract Submitted

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.