Product Recommendation Model Using Semantic Embeddings for Cold Start
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Solution Overview
Problem
Existing product recommendation systems face challenges in recommending related products for items with insufficient user history, leading to biased recommendations and inefficiencies in handling large-scale data, particularly in the shopping domain.
Innovation Solution
A related product recommendation model is developed using a natural language inference task to classify relations between sentences, leveraging a contrastive pair dataset and a Siamese network to generate embeddings that capture dynamic correlations between products, incorporating both history and content information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If history-based recommendation algorithms are used, then recommendation accuracy for items with sufficient history is improved, but recommendation capability for cold items deteriorates
Solution Approach 1:
The patent introduces a natural language processing intermediary that processes product titles and descriptions to generate semantic embeddings. This intermediary enables cold items without historical data to be represented and recommended based on their content semantics, bridging the gap between history-based accuracy and cold item capability
Solution Approach 2:
The patent transforms product information from traditional structured parameters (ID, category) to semantic parameters (embeddings from NLP models). This parameter transformation allows the system to leverage semantic similarity rather than relying solely on historical interaction data, enabling effective cold item recommendation
2Speed
If traditional recommendation models are used, then processing speed for simple queries is improved, but handling capability for large-scale data deteriorates
Solution Approach 1:
The patent performs preliminary processing by pre-computing semantic embeddings for all products using NLP models and storing them in advance. This preliminary action transforms raw text data into structured semantic representations, enabling efficient similarity search and recommendation generation without heavy real-time processing for large-scale data queries
Solution Approach 2:
The patent creates semantic copies (embeddings) of product information that capture the essential meaning without requiring the full original text data during recommendation generation. These copied representations enable fast comparison and matching across large product catalogs while reducing computational overhead
3Adaptability or versatility
If history-based recommendation systems are used, then personalization for users with extensive history is improved, but bias toward popular items deteriorates
Solution Approach 1:
The patent segments the recommendation approach into two independent components: user-specific historical behavior analysis and item-level semantic content analysis. By separating these functions, the system can personalize recommendations based on user history while simultaneously considering the intrinsic semantic value of items, thereby reducing bias toward popular items and enabling discovery of relevant but less popular products
Data Source
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AI summary
Disclosed are a method, a computer device, and a non-transitory computer-readable recording medium to recommend a related product. The method to recommend the related product may include constructing a contrastive pair dataset for a natural language inference task using content information related to a product based on history of the product in a shopping domain; and generating a product recommendation model to apply to the shopping domain through pre-training of a language model for the natural language inference task using the contrastive pair dataset.