Digital Content Recommendation via User Comment Clustering
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Solution Overview
Problem
Current digital content recommendation systems fail to effectively utilize user comments to provide personalized recommendations, as they do not adequately identify and cluster users with similar opinions, leading to suboptimal content suggestions.
Innovation Solution
A system that processes user comments to determine user preferences by identifying approving and disapproving terms, clusters users based on these preferences, and recommends digital content viewed and liked by similar users, ranking it based on similarity and user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user comments are processed to determine user preferences and cluster similar users, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments users into distinct clusters based on their comment patterns and preferences. By dividing the user base into homogeneous groups, the system can provide tailored recommendations for each cluster, improving recommendation accuracy while managing complexity through modular user group management
Solution Approach 2:
The system changes the parameter of user representation from basic demographic data to detailed preference profiles derived from comment analysis. By transforming raw comment data into structured preference parameters, the system achieves higher recommendation precision while the structured approach helps manage the complexity of processing unstructured text data
2Adaptability or versatility
If the system analyzes user comments to identify preferences, then personalization quality is improved, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of user comments to pre-determine user preferences and cluster users in advance. By conducting this analysis beforehand, the system reduces the processing time required during actual recommendation generation, as the preference profiles and user clusters are already established and ready for quick matching
Solution Approach 2:
The system creates simplified copies or representations of user preferences from detailed comment analysis. Once comprehensive preference profiles are built from comment processing, these profiles serve as reusable templates that can be quickly matched against content without re-analyzing the original comments, thus improving personalization quality while reducing repeated processing time
Data Source
AI summary
A method and system relate to receiving first comments, associated with first digital content, that are submitted by a first user, and determining an opinion of the first user with respect to the first digital content based on the one or more first comments. Determining the opinion of the first user with respect to the first digital content includes parsing the one or more first comments to determine a term included in the one or more first comments, and determining the opinion based on the term. The first user is clustered with second users who share the first users opinion regarding the first digital content. Second digital content, liked by at least one of the second users and have accessed by the first user, are identified, and a recommendation identifying the second digital content is presented for display to the first user.