Virtual Agent Item Identification Using Trend and Sentiment Scores
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Solution Overview
Problem
Virtual agents often inaccurately identify items of interest to users, leading to inefficient resource utilization and increased user queries, as they rely on incomplete information for suggesting products.
Innovation Solution
A management platform uses natural language processing and machine learning to determine trend scores and sentiment scores, identifying a subset of items that are more likely to be of interest to the user by analyzing popularity and user sentiment across various data sources, and provides these items as search results, while also determining return scores to optimize inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If virtual agents rely on incomplete information for suggesting products, then the device complexity is reduced, but the accuracy of item identification deteriorates
Solution Approach 1:
The system segments the complex data processing task into distinct components: trend score determination (popularity analysis), sentiment score determination (user feedback analysis), and return score determination (return probability analysis). Each component processes specific data sources independently and contributes to the final item identification, making the overall complex system manageable and effective
Solution Approach 2:
The management platform performs multiple functions using the same core processing architecture: it determines trend scores from various data sources, analyzes sentiment across different inputs, and calculates return probabilities. This multi-functional approach handles diverse data types (social media, reviews, sales data) through a unified system, reducing overall complexity while improving accuracy
2Loss of time
If virtual agents provide inaccurate item suggestions, then the ease of operation is maintained, but the loss of time increases due to increased user queries
Solution Approach 1:
The system performs preliminary analysis by determining trend scores, sentiment scores, and return scores before presenting items to users. This pre-processing filters and ranks items based on multiple criteria, so that when users query, they receive pre-validated suggestions that are more likely to be accurate, reducing the need for follow-up queries and saving time
3Measurement precision
If virtual agents use comprehensive data analysis, then the accuracy of item identification improves, but the use of energy increases
Solution Approach 1:
The system applies partial analysis by selectively processing data based on relevance. It determines trend scores from selected data sources, analyzes sentiment only for relevant items, and calculates return scores for candidate items. This partial processing approach maintains high accuracy by focusing computational energy on the most relevant data rather than exhaustively analyzing all available information
Data Source
AI summary
A first device may receive, from a second device, an input corresponding to a search for an item, may identify a set of items, and may determine a set of trend scores associated with the set of items. The first device may determine a set of sentiment scores, and may identify a subset of items, of the set of items, based on the set of trend scores and the set of sentiment scores. The first device may provide, to the second device, information associated with the subset of items to permit the second device to provide, for display, the information associated with the subset of items, and may receive, from the second device, information associated with a selected item, of the subset of items. The first device may determine a return score associated with the selected item, and may perform an action based on the return score.