Automated Semantic Tagging for E-commerce Product Recommendations
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Solution Overview
Problem
Existing e-commerce platforms face inefficiencies in manually tagging products with semantic tags, which are inconsistent, inconvenient, slow, and do not consider user interactions or product similarities, leading to inaccurate and static recommendations.
Innovation Solution
A system that automatically tags products by analyzing user search queries, interactions, and probabilistic models to determine weighted links between products and semantic tags, enabling dynamic and user-specific product recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual tagging is used by administrators and users, then product tags can be selected, but the process is slow, inconsistent, and does not consider user interactions
Solution Approach 1:
The system automatically generates semantic tags for products by analyzing user search queries and interactions without requiring manual intervention from administrators. The probabilistic model self-updates tag weights based on observed user behavior patterns, enabling the system to serve itself in the tagging process.
Solution Approach 2:
The patent replaces the mechanical manual tagging process with an automated computational system using probabilistic models and machine learning algorithms. The system substitutes human administrators' manual tag selection with automated analysis of search query data and user interaction patterns.
2Manufacturing precision
If manual tagging is used, then tags can be assigned to products, but the tags are inconsistent and do not reflect product similarities
Solution Approach 1:
The system continuously monitors user interactions with products and uses this feedback to update the weights of semantic tags. The probabilistic model adjusts tag associations based on observed patterns, ensuring tags remain consistent with actual user behavior and product relationships over time.
Solution Approach 2:
The system pre-computes semantic tag associations and weights based on historical search query data before users actually search for products. This preliminary analysis of user behavior patterns allows the system to have tags ready in advance, providing consistent and accurate tagging immediately when users search.
3Reliability
If manual tagging is used, then products can be categorized, but recommendations are static and inaccurate
Solution Approach 1:
The recommendation system dynamically adjusts semantic tag weights based on real-time user interaction data. Instead of static tags, the system continuously updates the relevance of tags to products based on observed user behavior, making recommendations adaptive and responsive to changing user preferences and trends.
Solution Approach 2:
The system changes the parameters of tag associations by using probabilistic weights instead of fixed binary tag assignments. The weights are continuously adjusted based on user interaction data, allowing the system to capture nuanced relationships between products and tags that improve recommendation accuracy.
4Loss of information
If semantic tags are determined based on previous search queries, then relevant tags can be identified, but the system requires complex analysis of user interactions
Solution Approach 1:
The system uses a single probabilistic model framework that simultaneously performs multiple functions: analyzing search queries, determining semantic tags, calculating tag weights, and generating recommendations. This multi-functional approach consolidates complex data processing into a unified system that handles diverse tasks efficiently.
Data Source
AI summary
Systems and methods for automatically tagging product for an e-commerce web application and providing product recommendations. Product information related to products is stored and the products are searchable via search queries. Results for the search queries are generated. Interactions of the users with the results for the search queries are monitored. Semantic tags are associated with the products based on the search queries and the results for the search queries. Weighted links between the products and the semantic tags are determined based on the interactions of the users with the results for the search queries. Users' interactions with the product information and/or the product are monitored and user links between the semantic tags and the users are determined based on the weighted links between the products and the semantic tags and the users' interactions. Product recommendations are determined based on the user links and the weighted links.