Personalized Network Service Recommendation via User Topic Segmentation
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Solution Overview
Problem
Current network service recommendation systems inaccurately recommend services to individual users as they rely on the interest standards of entire user groups, leading to a reduced accuracy rate in meeting the specific interests of single users.
Innovation Solution
A method and apparatus that retrieve historical browsing records to determine personalized topics based on browsing probability, generating recommended network service lists for each user, which are then used to recommend services tailored to their interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a backend system recommends network services to a single user according to an interest standard of an entire user group, then the recommendation process is simple and efficient, but the accuracy rate of meeting the specific interests of a single user is reduced
Solution Approach 1:
The patent segments the entire user group into multiple interest groups based on historical browsing records and topic models. Instead of treating all users uniformly, the system divides users into distinct segments (interest groups) with similar preferences. This allows recommendations to be tailored to each segment's specific interests while maintaining operational efficiency through automated clustering.
Solution Approach 2:
The patent applies local quality by providing different recommendation strategies to different user segments. Each interest group receives recommendations based on its specific topic preferences rather than a universal approach. The system identifies the user's specific interest group and applies localized recommendation logic appropriate to that group's characteristics, improving accuracy for individual users.
2Measurement precision
If personalized topic models are built for each user based on historical browsing records, then the recommendation accuracy for individual users is improved, but the system complexity increases
Solution Approach 1:
The patent extracts common topic patterns from individual user browsing records to form interest groups. Instead of building completely separate models for each user, the system extracts shared characteristics and clusters users with similar extracted topics. This reduces complexity by identifying commonalities while still capturing individual preferences through the interest group classification.
Solution Approach 2:
The patent changes the parameter of user representation from individual detailed browsing histories to aggregated interest group memberships. By transforming user data into interest group classifications based on topic models, the system reduces the dimensionality and complexity of personalization while maintaining recommendation accuracy through group-based preferences.
Data Source
AI summary
The present disclosure discloses a network service recommendation method and apparatus, which belong to a network data analysis technology. The method includes: retrieving, according to a historical browsing record of a user during use of a network service, a label corresponding to each network service used by the user; determining, according to a preset label-topic correspondence, and by using the label corresponding to each network service used by the user, first n topics corresponding to the user; acquiring, according to a preset topic-network service correspondence, respective corresponding recommended network service lists of the first n topics, the recommended network service list of each topic including at least one network service; and recommending a network service to the user according to the respective corresponding recommended network service lists of the first n topics.