Sentiment Summarization Modules for Personalized Product Recommendations
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Solution Overview
Problem
Current systems fail to effectively utilize user-generated post-purchase content to provide personalized product recommendations and customer segmentation in e-commerce, leading to suboptimal customer experiences and inefficient product discovery.
Innovation Solution
A system that generates weighting vectors based on user intent and sentiment data from post-purchase content to recommend items and categorize users, using sentiment summarization modules and intent weight inputs to personalize product listings and improve customer navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user-generated post-purchase content is collected and analyzed, then recommendation accuracy and customer segmentation quality improve, but system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces sentiment summarization modules as intermediary components that process and aggregate user-generated post-purchase content into structured sentiment data. These modules act as mediators between raw user feedback and the recommendation engine, transforming unstructured data into actionable insights without requiring the entire system to directly handle complex raw data processing.
Solution Approach 2:
The system segments the analysis process into distinct modules: sentiment analysis modules that evaluate individual aspects of products, summarization modules that aggregate sentiments, and recommendation modules that apply the processed data. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while managing complexity through modular architecture.
2Ease of operation
If real-time personalized recommendations are provided, then customer experience and engagement improve, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary sentiment analysis on user-generated post-purchase content and pre-computes recommendation scores for various product aspects before actual recommendation requests arrive. By preparing sentiment summaries and pre-calculating recommendation probabilities in advance, the system reduces the computational burden during real-time interactions, enabling fast personalized recommendations without excessive resource consumption.
3Reliability
If comprehensive user intent and sentiment data are processed, then product discovery quality improves, but data processing time and computational load increase
Solution Approach 1:
The patent extracts only the most relevant sentiment aspects and user intent indicators from comprehensive post-purchase content, rather than processing all available data. The sentiment summarization modules selectively identify and extract key sentiment dimensions (such as product quality, value for money, durability) that are most predictive of recommendation accuracy, discarding redundant information to reduce processing time while maintaining discovery quality.
Data Source
AI summary
A method implemented via execution of computing instructions configured to run at one or more processors and stored at non-transitory computer-readable media. The method can include sending to a user an input form comprising an input element for a respective intent weight for each of a plurality of features. The method also can include receiving from the user the respective intent weights for the plurality of features. The method additionally can include selecting one or more first items from among a plurality of items in the category of items based at least in part on: (a) the respective intent weights for the plurality of features for the user, and (b) sentiment data comprising a respective sentiment score for each respective feature for each of the plurality of items. The method further can include displaying the one or more first items to the user in a graphical user interface in real-time after receiving the respective intent weights. The method additionally can include updating the graphical user interface in an interactive sequence based on a selection received from the user of one of the one or more first items. Other embodiments are described.