Mining Product Recommendations from Query Reformulations
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Solution Overview
Problem
Conventional product recommendation systems in e-commerce platforms are limited to suggesting complementary items based on past purchase history, failing to provide tailored recommendations when users transition between different products during a search session.
Innovation Solution
A system that collects and processes session sets of user queries to identify search transitions, updating transition data to recommend more relevant products by incrementing a transition count between products, allowing for personalized recommendations based on current user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If product recommendations are based on past purchase history, then complementary items can be identified, but the recommendations fail to provide tailored suggestions when users transition between different products during a search session
Solution Approach 1:
The system performs preliminary action by collecting and processing session sets of user queries in advance to identify search transitions and update transition data before providing recommendations. This allows the system to be prepared with transition information without requiring real-time processing during the actual recommendation generation, thereby capturing user intent changes while maintaining system efficiency.
2Productivity
If conventional purchase history recommendations are used, then simple complementary item identification is achieved, but additional user searching is required and computing resources are wasted
Solution Approach 1:
The system implements feedback by analyzing user search queries and session behavior to identify transitions between products. This feedback loop allows the recommendation system to continuously learn from actual user search patterns and update transition data, thereby providing more accurate recommendations that reduce the need for additional searching and minimize computing resource consumption by targeting only relevant products.
Data Source
AI summary
In various example embodiments, systems and methods to mine product recommendations from query reformulation is provided. In example embodiments, a search query is mapped to a particular product. Transition data for the particular product is accessed. The transition data includes values reflecting a number of search transitions between the particular product and other products in session sets of previous queries. One or more recommendations based on the transition data is determined and provided to the user.