Vector Space Product Similarity Identification
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Solution Overview
Problem
Conventional product recommendation techniques in online commerce fail to effectively identify and present the most relevant range of options within a product category, often missing similar products from different manufacturers or service providers that may interest consumers.
Innovation Solution
The technique represents products as vectors in a vector space, allowing for the identification of similar products by calculating distances between product vectors, with dynamic adjustment of process parameters based on user actions to refine recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional product recommendation techniques using consumer demographics and online behavior tracking are used, then basic product recommendations can be generated, but the accuracy and relevance of recommendations within specific product categories is insufficient
Solution Approach 1:
The patent transforms product attributes into numerical vectors in a multi-dimensional space, enabling mathematical comparison and distance calculation. This parameter transformation allows for precise measurement of product similarity by calculating Euclidean distances between product vectors, directly improving recommendation accuracy within specific categories.
Solution Approach 2:
The patent introduces a vector space dimensionality framework where products are represented as points in multi-dimensional space based on their attributes. This dimensional transformation enables the system to capture nuanced product similarities that conventional flat comparison methods miss, particularly within specific product categories.
2Productivity
If product recommendations are limited to obvious similar products, then recommendation speed is maintained, but the diversity and range of relevant options presented to consumers is reduced
Solution Approach 1:
The patent implements dynamic adjustment of the distance threshold parameter based on user interactions. As users view and interact with recommended products, the system adapts the similarity threshold to expand or contract the range of recommended products, balancing speed and diversity based on real-time user behavior patterns.
Solution Approach 2:
The system incorporates feedback loops where user interactions with recommended products (views, clicks, purchases) are fed back into the recommendation engine. This feedback mechanism allows the system to learn from user behavior and adjust future recommendations, maintaining both speed and diversity by adapting to individual user preferences.
Data Source
AI summary
Methods and apparatus are described for identifying similar products or services for the purpose of making relevant recommendations to an online consumer. Products and services are represented by associated vectors which include values for each of a plurality of attributes of the corresponding product or service. One or more similar products or services are identified relative to a reference product or service set with reference to the distance between the end points of the respective vectors in the associated vector space.