Cold-Start Recommendation via Meta-Mapped Preference Features
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Solution Overview
Problem
Conventional object recommendation methods fail to accurately recommend objects in a 'cold start' state due to a lack of interaction data, leading to ineffective user interaction.
Innovation Solution
A method involving multilayer perceptron processing on content and initial preference features using a meta-mapping network to transform the features into an updated preference feature that matches the target preference feature spatial distribution, enabling accurate recommendation analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional object recommendation method is used, then recommendation system can operate with existing interaction data, but accurate recommendation cannot be implemented for cold start objects due to lack of interaction data
Solution Approach 1:
The patent introduces a meta-mapping network as an intermediary component that bridges the gap between cold start objects and the recommendation system. This network maps content features of cold start objects to preference feature space, enabling accurate recommendations without requiring extensive interaction data. The meta-mapping network serves as a mediator that translates object characteristics into meaningful preference representations.
Solution Approach 2:
The patent performs preliminary action by pre-training the meta-mapping network using simulated cold start data and existing interaction patterns before actual recommendation occurs. This preliminary training enables the system to handle cold start objects accurately from the outset, as the network already understands the mapping between content features and preference distributions.
2Reliability
If multilayer perceptron processing with meta-mapping network is applied, then accurate recommendation for cold start objects is achieved, but system complexity increases
Solution Approach 1:
The meta-mapping network is designed with multi-functionality, serving both as a feature transformation layer and as a preference distribution mapping layer. This universal component handles multiple tasks within a single structure, reducing the need for separate specialized modules and thereby limiting the increase in system complexity despite the added capability for accurate cold start recommendations.
Data Source
AI summary
This application relates to a method for recommending a cold start object performed by a computer device. The method includes: obtaining a content feature and an initial preference feature that are of a target object in a cold start state; performing multilayer perceptron processing on the content feature based on a meta-mapping network to obtain a feature mapping relationship matching the target object; performing a mapping transformation on the initial preference feature according to the feature mapping relationship, to obtain an updated preference feature close to a target preference feature spatial distribution; and performing recommendation analysis on the target object based on the updated preference feature, to obtain a recommendation result for the target object.