Multi-feature Search–based Purchasing Tendency Community Classification For Densely Distributed Clients In E-commerce

Abstract

Purchasing Tendency Is Defined As Customer Prefer-ences For Products And Brands, Interested In Price And Frequency Of Purchase, And Is Determined By Demographic, Transactional And Behavioral Attributes. In Today’s E-commerce, These Insights Are Critical For Recommendations And Managing Demand. But Conventional If-then Rules And Elementary Collaborative Filtering Approaches Lack Sophisticated Insights Into Interactions Between Customers, Products, And Locations, And The Demand At Different Times. To Overcome These Challenges, This Article Proposes A Community Classification System Of Clients Purchasing Inventory Using A Holistic Deep Learning Approach. Graph Neural Networks (GNNs) Capture The Relationship Between Customers, Products, And Regions, Facilitating Precise Identification Of Customer Com-munities And Region-based Demand (high, Emerging, Low). The SASRec Transformer-based Model Also Leverages Temporal In-formation About Customer Preferences By Training On Temporal Sequences, Capturing Both Short- And Long-term Information. This Approach Incorporates Demographic, Transactional And Be-havioral Data To Offer Insights To Sellers And Recommendations To Customers, Thus Improving Decision-making, Forecasting, And Efficiency In The Market.

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