Segment-Based Recommendation Engine Using Vector Space Models
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Solution Overview
Problem
Current product recommendation engines in the grocery segment fail to effectively update recommendations based on the current basket content during the shopping journey, leading to missed sales opportunities due to reliance on customer segmentation and shopping history rather than real-time basket data, and generic recommendations for lack of focused customer data.
Innovation Solution
A segment-based and cross-retail recommendation system that uses item codes to determine similar items through a vector space model, adjusting recommendations in real-time based on the current basket content, leveraging 'Word2Vec' algorithms to analyze transaction data and provide focused, accurate suggestions across multiple retailers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current recommendation engines rely on customer segmentation and shopping history, then recommendations can be provided based on available data, but the recommendations fail to update based on current basket content and are not personalized enough
Solution Approach 1:
The recommendation system dynamically updates recommendations as the basket evolves during the shopping journey. The system processes item codes in real-time and adjusts recommendations based on the current basket composition rather than relying solely on static customer segmentation data, making the recommendation process adaptive and responsive to changing customer intent.
Solution Approach 2:
The system pre-processes transaction data to build retailer-specific vector spaces that encode item relationships and customer preferences. This preliminary processing enables the system to quickly compute accurate recommendations when item codes are provided, without needing to re-analyze all historical data at recommendation time.
2Adaptability or versatility
If recommendation engines use customer-specific data, then personalized recommendations can be provided, but the customer data may be voluminous and lack focus on specific demographics
Solution Approach 1:
The system segments the large voluminous customer data into retailer-specific vector spaces that capture demographic and behavioral patterns for each retailer. This segmentation organizes the complex data into structured representations that enable focused, personalized recommendations while reducing the computational burden of processing raw voluminous data.
Solution Approach 2:
The system transforms raw customer transaction data into vector space representations with specific parameters that capture essential patterns. By changing the data representation from raw transactions to structured vector embeddings, the system maintains personalization capability while simplifying the complexity of data processing.
3Reliability
If the system processes all item codes including those without barcodes, then complete transaction data is analyzed, but items without barcodes cannot be directly mapped in the vector space
Solution Approach 1:
The system uses similar item codes with barcodes as intermediaries to represent items without barcodes in the vector space. By identifying and replacing non-barcoded item codes with their barcoded equivalents based on similarity, the system maintains data completeness while enabling vector space mapping for all items.
Data Source
AI summary
Transaction items for a transaction are received during a transaction. Any non-barcoded items are identified and processed within a retailer-specific vector space to identify a most-similar barcoded item offered by a corresponding retailer to the non-barcoded item. The transaction items are revised to include the most-similar barcoded item as a replacement for the non-barcoded item. The revised transaction item list is used to identify a recommended item based on a segment-specific vector space associated with a segment assigned to the transaction. The recommended item is provided in real time to a transaction service that processes the transaction for delivery to a customer associated with the transaction.