Cross-Domain Content Recommendation for Cold-Start Users
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Solution Overview
Problem
Personalized recommendation systems face challenges with data sparseness and cold start problems, where insufficient historical interaction data makes it difficult to accurately analyze user interests and preferences, leading to inefficient resource utilization.
Innovation Solution
A content recommendation method that utilizes transfer learning by extracting feature representations from multiple domains, performing clustering analysis, and establishing personalized mapping functions to map outside-domain features to intra-domain features, enabling accurate content recommendation for users with limited interaction history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a personalized recommendation system collects user attributes and historical interaction data, then content recommendation accuracy is improved, but the system cannot accurately analyze user interests when historical interaction data is insufficient (cold start problem and data sparseness problem)
Solution Approach 1:
The patent introduces a transfer learning mechanism that acts as an intermediary between source domain data (other platforms) and target domain data (current platform). The system extracts feature representations from source domains and uses a mapping function to transfer them to the target domain, enabling accurate recommendation even when target domain historical data is insufficient. This intermediary approach resolves the contradiction by allowing the system to leverage external data sources to compensate for insufficient internal historical data.
Solution Approach 2:
The patent extends the data collection dimension from only the target platform to multiple source platforms. By extracting features from historical interaction data across different platforms and domains, the system creates a multi-dimensional data space that enriches the user profile. This dimensional expansion allows the system to overcome the data sparseness problem in the target domain by incorporating information from other dimensions (other platforms).
2Productivity
If the system pushes content to users without accurate interest analysis, then resource utilization is low, but collecting and processing more user data increases system complexity
Solution Approach 1:
The transfer learning mapping function serves as an intermediary that efficiently bridges source domain features and target domain requirements. Instead of collecting extensive data from the target platform, the system uses the mapping function to translate relevant features from source domains, reducing the complexity of data collection and processing while improving recommendation accuracy and resource utilization efficiency.
Solution Approach 2:
The system changes the approach from collecting raw historical interaction data to extracting and transforming feature representations. By parameterizing the data through feature extraction and mapping functions, the system reduces the volume and complexity of data that needs to be processed, while maintaining high recommendation accuracy. This parameter transformation approach improves resource utilization without proportionally increasing system complexity.
Data Source
AI summary
A content recommendation method is provided. A first feature representation of a first user and a second feature representation of a second user are obtained. A cluster center corresponding to the first user is determined. An extra-domain feature representation and intra-domain feature representation of the first user are obtained. The intra-domain feature representation is based on mapping the extra-domain feature representation with the mapping relationship function. A target feature representation of the first user is determined based on the intra-domain feature representation and the first feature representation. Target content that matches the target feature representation of the first user is determined. The target content is pushed to the first user.