Document Recommendation via Transfer Learning for Cold Start
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Solution Overview
Problem
Traditional document recommendation systems face challenges in providing personalized recommendations to new users due to the 'cold start problem', as they rely heavily on user feedback and content analysis, which becomes cumbersome when few data points are available, and collaborative filtering struggles to define accurate correlation functions.
Innovation Solution
A document recommendation method based on transfer learning that utilizes knowledge from source users' data to create a target recommendation model, incorporating subspace importance to adapt to different user preferences, thereby overcoming the cold start issue by normalizing the recommendation task as a regularized transfer learning problem with consistency subspace constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional document recommendation approaches use content analysis and user feedback, then recommendation accuracy can be improved for users with sufficient data, but the system becomes cumbersome and difficult to operate when few data points are available
Solution Approach 1:
The patent applies preliminary action by pre-training a recommendation model on source user data before it is needed for target users. The system performs advance learning from available source data, creating a pre-trained model that can be quickly adapted to new users without requiring extensive data collection or complex operations at the time of recommendation.
Solution Approach 2:
The patent uses transfer learning as an intermediary mechanism that bridges source user data and target user recommendations. Instead of directly analyzing target user behavior (which may be insufficient), the system uses source user patterns as an intermediate step to generate accurate recommendations for target users.
2Productivity
If collaborative filtering is used to compute similarity between users and documents, then recommendations can be generated, but defining an accurate correlation function becomes difficult when data points are limited
Solution Approach 1:
The patent merges source user data with target user data in a unified transfer learning framework. By combining information from multiple users (source and target) rather than analyzing target user data in isolation, the system overcomes the limitation of limited data points and achieves accurate correlation measurement.
Solution Approach 2:
The patent changes the parameters of the recommendation model by adapting pre-learned parameters from source users to target users through transfer learning. This parameter adaptation allows the system to generate accurate recommendations even when target user data is limited, as the model parameters are refined rather than learned from scratch.
3Adaptability or versatility
If personalized recommendation is implemented for new users, then user experience can be improved, but the cold start problem arises when insufficient historical data is available
Solution Approach 1:
The patent applies copying by replicating the recommendation model structure and initial parameters from source users to target users. The system creates a copy of the learned patterns from source users and adapts it for target users, enabling personalized recommendations to work reliably even when target user data is insufficient.
Solution Approach 2:
The system performs preliminary training on source user data to establish a reliable baseline model before applying it to target users. This advance preparation ensures that personalized recommendations can be provided reliably from the start, eliminating the cold start problem.
Data Source
AI summary
Embodiments of the present disclosure provide a method and apparatus for document recommendation by obtaining a plurality of first data for a source recommendation task from activities related to a source user using a plurality of first documents; obtaining a plurality of second data for a target recommendation task from activities related to a target user using a plurality of second documents; performing the target recommendation task based on the plurality of first data, the plurality of second data, and knowledge transferred from the source recommendation task to obtain a target recommendation model; and conducting document recommendation to the target user using the target recommendation model.