Conversation Session Item Recommendation via Importance Extraction
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Solution Overview
Problem
Existing item recommendation models based on conversation sessions struggle to accurately capture user preferences when user-item interaction histories are unavailable, often focusing on irrelevant items in long conversation sessions.
Innovation Solution
An item recommendation method and system that prioritize the importance of items in a conversation session by obtaining item embedding vectors, calculating importance representations, and combining them with long-term and current user preferences to accurately recommend target items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing item recommendation models focus on all items in conversation session equally, then the model can process complete conversation data, but irrelevant items negatively affect recommendation accuracy
Solution Approach 1:
The patent extracts and identifies important items from the conversation session using an attention mechanism, separating them from irrelevant items. The importance representation module calculates attention scores for each item based on user preferences and conversation context, then selectively focuses on high-importance items for recommendation, eliminating the negative impact of irrelevant items on accuracy
Solution Approach 2:
The patent applies different processing weights to different items in the conversation session based on their importance. The attention mechanism assigns higher weights to important items and lower weights to irrelevant items, creating a non-uniform processing quality that improves recommendation accuracy without requiring complex manual filtering
2Loss of information
If the model processes all items in long conversation sessions with equal attention, then complete information is captured, but calculation complexity increases significantly
Solution Approach 1:
The patent applies partial action by focusing computational resources only on important items rather than processing all items equally. The attention mechanism identifies a subset of important items from the full conversation session, performing detailed analysis only on these selected items while using simplified processing for others, thereby reducing overall calculation complexity while maintaining information completeness
3Adaptability or versatility
If user-item interaction histories are unavailable, then the model must rely solely on current conversation session, but accurately capturing user preference becomes challenging
Solution Approach 1:
The patent performs preliminary action by pre-defining user preference representations and attention mechanisms that can operate effectively with only current conversation data. The model pre-processes the conversation session to identify important items and infer user preferences from contextual patterns, enabling accurate recommendations even without historical interaction data
Data Source
AI summary
The present disclosure provides an item recommendation method and system based on importance of item in a conversation session and a system thereof. In the present disclosure, an importance extracting module extracts an importance of each item in the conversation session, and then a long-term preference of a user is obtained in combination with the importance and the corresponding item, and then a preference of the user is obtained accurately in combination with a current interest and the long-term preference of the user, and finally item recommendation is performed according to the preference of the user. In this way, the accuracy of the item recommendation is improved.