Forum Recommendation System Using Weighted User Behavior Data Integration
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Solution Overview
Problem
Existing content recommendation methods in forums are inaccurate due to oversimplification of user interests and scarcity of data, leading to poor recommendation results, especially in small forums with limited user browsing behavior data.
Innovation Solution
A method that integrates search behavior, browsing behavior, and click behavior data through preprocessing and weighted distribution to provide more accurate content recommendations, enriching the data used in recommendations and improving their accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple user behavior data types are integrated, then recommendation accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments user behavior data into three distinct types: search behavior data, browsing behavior data, and click behavior data. Each type is processed separately through dedicated preprocessing modules that generate individual recommendation results, which are then integrated through a weighted distribution mechanism. This segmentation approach enables comprehensive data utilization while maintaining manageable processing complexity through modular architecture.
Solution Approach 2:
The patent merges multiple sources of user behavior data (search, browsing, and click data) into a unified recommendation system. By combining these different data types and their respective recommendation results through weighted distribution, the system achieves more accurate and comprehensive content recommendations that leverage the strengths of each data source.
2Measurement precision
If multiple user behavior data types are integrated, then recommendation accuracy is improved, but system operation complexity increases
Solution Approach 1:
The system implements automated preprocessing operations for each type of user behavior data, where the preprocessing modules automatically generate recommendation results from their respective data sources without requiring manual intervention. The weighted distribution mechanism automatically integrates these results, reducing operational complexity while maintaining high recommendation accuracy.
3Measurement precision
If weighted distribution integration is applied, then recommendation accuracy is improved, but calculation complexity increases
Solution Approach 1:
The patent introduces weight parameters to control the contribution of each recommendation result type in the final integration. By adjusting these weight parameters, the system can optimize recommendation accuracy while managing calculation complexity. The weighted distribution mechanism uses these parameters to efficiently combine multiple recommendation sources without requiring overly complex computational algorithms.
Data Source
AI summary
The present disclosure discloses a method, apparatus, and server for information recommendation. Search behavior data, browsing behavior data, and click behavior data on recommended content of a specified user in a forum are acquired. A preprocessing on the search behavior data, the browsing behavior data, and the click behavior data on recommended content is performed respectively to obtain a first recommendation result, a second recommendation result, and a third recommendation result. Distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result are performed according to weights to obtain recommended content to be recommended to the specified user. Search behavior data, browsing behavior data, and click behavior data on recommended content are taken into comprehensive consideration, data used in recommendation is enriched, and accuracy of recommendation is improved.