Basket-Aware Item Recommendation System for Checkout Category Balance

The system segments recommended items into distinct categories (repurchase items, low consideration complimentary items, high consideration complimentary items) and applies separate selection and display logic for each category. This segmentation ensures that items from different categories are balanced in the recommendations, preventing any single category from dominating the display while maintaining overall relevance.

The system divides the recommendation generation process into separate modules for each item category, where each module independently processes and selects items for its specific category. This segmentation of the computational process reduces the overall complexity by breaking down the complex task into smaller, more manageable sub-tasks that can be executed in parallel or sequence with reduced computational overhead.

The system applies different display strategies to different categories of items based on their characteristics. High consideration items are displayed with fewer quantities and enhanced information presentation compared to low consideration items. This local quality adjustment ensures that each category is presented in a manner appropriate to its nature, reducing overall decision complexity while maintaining recommendation completeness.

The system performs preliminary analysis and categorization of basket items before generating recommendations. By pre-processing the basket data and identifying item categories in advance, the system reduces the computational burden during the actual recommendation generation phase, thereby maintaining high personalization accuracy while reducing processing time.