Item Recommendation Matrix Using Pre-Use Preference Inference
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Solution Overview
Problem
Conventional item recommendation systems suffer from low accuracy due to the oversight of unrated items, which can provide valuable information about user preferences, as they often rely solely on rated items and fail to distinguish between preferred and unpreferred items.
Innovation Solution
The method generates a rating matrix based on user-item interactions, infers pre-use preferences for unrated items by changing post-use preferences in the rating matrix, and creates a zero-injected matrix to exclude uninteresting items by assigning zero ratings to those with low pre-use preferences, thereby improving recommendation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional item recommendation systems rely solely on rated items to predict user preferences, then the recommendation process is simple, but the accuracy of recommendations is low
Solution Approach 1:
The patent segments unrated items into two distinct categories: uninteresting items (assigned zero ratings) and potentially interesting items (assigned inferred ratings). This segmentation allows the system to differentiate between items users actively disregarded versus items users simply haven't encountered, thereby improving recommendation accuracy without overwhelming system complexity
Solution Approach 2:
The patent performs preliminary action by inferring pre-use preferences for unrated items before the recommendation process. By predicting whether users would find items interesting before they rate them, the system proactively enriches the rating matrix, transforming cold-start unrated items into usable recommendation signals
2Measurement precision
If the system considers unrated items to improve recommendation accuracy, then more information about user preferences can be utilized, but the system may recommend uninteresting items
Solution Approach 1:
The patent applies preliminary anti-action by assigning zero ratings to uninteresting unrated items before the recommendation process begins. This preemptive measure prevents uninteresting items from being recommended, while still allowing potentially interesting unrated items to be considered based on inferred preferences
Solution Approach 2:
The patent converts the harmful effect of unrated items (which could be uninteresting and reduce reliability) into a benefit by inferring pre-use preferences. The previously problematic unrated data is transformed into valuable preference signals that improve both accuracy and reliability simultaneously
3Loss of information
If the system uses only post-use preferences from rated items, then the implementation is straightforward, but valuable preference information is lost
Solution Approach 1:
The patent performs preliminary action by inferring pre-use preferences for unrated items before generating recommendations. This allows the system to capture preference information that would otherwise be lost, converting the sparse rating matrix into a more informative structure
Solution Approach 2:
The patent adds another dimension to the preference data by introducing pre-use preference inference alongside post-use ratings. This creates a multi-dimensional view of user preferences, transforming the flat rating matrix into a richer preference matrix that captures both observed and inferred preferences
Data Source
AI summary
An item recommendation method and apparatus are provided. The item recommendation method and apparatus may recognize items preferred before a user uses items, based on items rated by the user, and may recommend an item to the user based on preferences for the recognized items.