Recommendation Reason Generation Using Q&A-Guided User Interest Mining
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing commodity recommendation methods fail to accurately detect user interests and generate effective recommendation reasons due to reliance on noisy review information or limited content analysis, leading to poor user experience and inefficient labor costs.
Innovation Solution
A method and apparatus that utilize pre-trained recommendation reason generation models incorporating question-answer data and content information to generate targeted recommendation reasons, leveraging encoders like LSTM and transformers to encode content and question-answer data, and a hierarchical multi-source posterior network for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If noisy review information is used for commodity recommendation, then the recommendation system can operate with limited content analysis, but the accuracy of detecting user interests and generating recommendation reasons deteriorates
Solution Approach 1:
The patent extracts clean, structured data from noisy review information by separating useful content (questions and answers about product aspects) from irrelevant noise. The system identifies and extracts specific product aspects mentioned in Q&A pairs, creating a purified dataset that maintains ease of implementation while significantly improving the accuracy of user interest detection and recommendation reason generation.
2Device complexity
If traditional recommendation methods are used, then the system structure remains simple, but the quality of recommendation reasons and user experience deteriorates
Solution Approach 1:
The patent segments the recommendation system into distinct functional modules: a data processing module that cleans and structures review information, an aspect extraction module that identifies product features from Q&A pairs, and a recommendation generation module that creates personalized recommendations. This segmentation maintains relative system simplicity while dramatically improving recommendation quality by assigning specific functions to each module.
Solution Approach 2:
The patent introduces an intermediary processing layer between raw review information and the recommendation engine. This intermediary module cleans, structures, and extracts meaningful aspects from noisy data before feeding it to the recommendation system, thereby improving recommendation quality without requiring fundamental changes to the overall system architecture.
3Measurement precision
If manual analysis of product information is used, then recommendation accuracy can be improved, but labor costs and processing time increase significantly
Solution Approach 1:
The patent implements a self-service automated system that performs data cleaning, aspect extraction, and recommendation generation without manual intervention. The system automatically identifies product aspects from Q&A pairs, extracts relevant features, and generates personalized recommendations, achieving high accuracy comparable to manual analysis while dramatically improving processing efficiency and reducing labor costs.
Solution Approach 2:
The patent replaces manual mechanical analysis with an automated computational system. Instead of human analysts manually reviewing product information and generating recommendations, the system uses automated text processing, pattern recognition, and machine learning algorithms to perform the same functions at scale, maintaining high accuracy while eliminating labor-intensive processes.
Data Source
AI summary
The embodiment of the present application provides a method and apparatus for generating a recommendation reason, a device and a storage medium. Content information of an object to be recommended is acquired, and the recommendation reason of the object to be recommended is generated according to a pre-trained recommendation reason generation model and the content information, where the training data used by the recommendation reason generation model includes question-answer data and content information of multiple objects. In this technical solution, since the training of the recommendation reason generation model takes the question-answer data and the content information of multiple objects into account, commodities that users care about most are mined through the question-answer data, therefore, the recommendation reason generated by this solution can accurately target users' needs and improve user experience.