Neural Network Shopping Agent Bot for Complex Product Recommendations
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Solution Overview
Problem
Existing conversational recommender systems (CRS) struggle to effectively recommend complex products with multiple attributes, as they rely on prior user data which is often lacking for first-time purchases, and require significant expertise and salesmanship, limiting their ability to provide informed recommendations.
Innovation Solution
A simulation-based training framework using neural network models to generate conversational data, where a Shopper bot and Seller bot simulate interactions, leveraging a product catalog and buying guide to educate and recommend complex products, improving recommendation performance and educational value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing conversational recommender systems use prior user data for recommendations, then recommendation accuracy improves, but the system fails for first-time purchases where no prior data exists
Solution Approach 1:
The system performs preliminary educational conversations with shoppers before making recommendations. The seller bot conducts multiple turns of conversation to educate the shopper about product categories, attributes, and preferences, building a knowledge base before the actual recommendation phase. This preliminary action enables the system to handle first-time purchases effectively.
Solution Approach 2:
The system introduces an intermediary educational phase between the shopper's initial query and the final recommendation. During this intermediate phase, the seller bot asks guiding questions and provides product information to help the shopper understand their needs, serving as a bridge that enables accurate recommendations without relying on prior purchase history.
2Measurement precision
If complex products require significant salesperson expertise for informed recommendations, then recommendation quality improves, but the system becomes difficult to operate without human intervention
Solution Approach 1:
The seller bot is designed to autonomously conduct educational conversations and provide recommendations without requiring human salesperson intervention. The system self-manages the multi-turn dialogue, adapts to shopper responses, and independently generates personalized recommendations, making the system easy to operate while maintaining high recommendation quality through automated expertise.
Solution Approach 2:
The system continuously monitors shopper responses and adjusts its educational approach based on feedback. The seller bot asks questions, receives shopper responses, and iteratively refines the recommendation based on this feedback loop, enabling the system to handle complex products with the same effectiveness as human experts while maintaining operational simplicity.
3Loss of information
If the system provides educational conversations about complex products, then shopper understanding improves, but the conversation length and time increase
Solution Approach 1:
The system provides partial educational action focused only on the specific product category and attributes relevant to the shopper's query, rather than comprehensive education across all possible topics. The seller bot identifies and addresses only the necessary educational points needed for informed recommendations, reducing unnecessary conversation time while maintaining sufficient shopper understanding.
Solution Approach 2:
The educational conversation dynamically adapts its length and depth based on shopper responses and needs. The seller bot adjusts the number of turns, detail level, and topic focus in real-time, providing more education when needed and reducing conversation time when the shopper demonstrates sufficient understanding, thus optimizing the balance between information delivery and time efficiency.
Data Source
AI summary
In view of the need for a conversational recommender system (CRS) in guiding purchasing processes of complex items, embodiments described herein provide a CRS system that creates a realistic purchase scenario and agent evaluation for fulfilling the recommendation objective. Specifically, the CRS system utilizes existing buying guides as a knowledge source for the recommendation model.