Dynamic Product Recommendation Engine Using Contextual Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems for providing product recommendations on e-commerce websites are static and unable to adjust based on contextual information such as user interests or purchase history, leading to a lack of personalized recommendations, which can reduce user experience and profitability for retailers.
Innovation Solution
A computer-implemented system that uses a machine learning model to rank products based on contextual information associated with users, including search queries, user preferences, and product features, allowing for dynamic and personalized product recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional static product models are used to provide recommendations, then the system is simple to implement, but the recommendations cannot be personalized based on user context
Solution Approach 1:
The patent transforms static product models into dynamic recommendation systems by incorporating real-time contextual information (user profile, search query, product features) to generate personalized recommendations. The system dynamically adjusts recommendations based on changing user context without requiring complex custom models for each user.
Solution Approach 2:
The patent creates a universal recommendation engine that handles multiple functions: basic product matching, contextual filtering, scoring, and ranking. This single system serves all users with personalized recommendations without requiring separate models for each user, achieving personalization through multi-functional processing rather than multiple models.
2Adaptability or versatility
If new product models are created for each user based on their interests, then personalized recommendations can be provided, but it consumes extremely large amounts of storage and processing time
Solution Approach 1:
Instead of creating complete custom models for each user (excessive action), the system uses partial customization by layering contextual information (user profile, search query, product features) on top of a base product model. This partial approach achieves personalization without the computational overhead of full custom models for each user.
Solution Approach 2:
The patent segments the recommendation process into distinct components: base product matching, contextual filtering, scoring, and ranking. By dividing the system into modular segments that process different aspects of personalization separately, it avoids the need to create and store complete custom models for each user, significantly reducing storage and processing requirements.
3Ease of operation
If static product recommendations are provided, then the system is fast to implement, but user experience and retailer profits decrease
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing product features and user profiles before recommendation generation. This preparation work is done in advance, allowing the system to quickly generate personalized recommendations during actual use without sacrificing implementation speed or requiring complex real-time computations.
Data Source
AI summary
Systems and methods are provided for providing product recommendations, comprising receiving, from a user device, a search query, defining, using a query handler cluster, context associated with the search query, identifying, using a search cluster, one or more products associated with the defined context, filtering, using the search cluster, the one or more products associated with the defined context based on features associated with the one or more products stored in a database, ranking, using the search cluster, the one or more products associated with the defined context based on a score generated for each product, and displaying, on the user device, product recommendations, wherein the product recommendations comprise a number of the one or more ranked products.