Annotation-Based Content Ranking via User Engagement Metrics
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Solution Overview
Problem
Existing systems for electronic books lack an effective method to rank content items based on user annotations, failing to accurately reflect popularity and emphasizing recent annotations over time.
Innovation Solution
A cloud-based system calculates a ranking for electronic books by combining a logarithmic function based on the number of users making annotations with an exponential function based on the geometric mean of annotations per user, while applying an exponential decay to annotation contributions over time to emphasize recent annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a simple count of annotations is used to rank electronic books, then the ranking system is easy to implement, but it fails to accurately reflect popularity and user engagement
Solution Approach 1:
The patent transforms the simple annotation count into a weighted score by changing the parameters considered: it incorporates the number of unique users, the frequency of annotations per user, and the recency of annotations. This parameter transformation allows the system to accurately reflect popularity without requiring overly complex mechanisms, as it builds upon the existing annotation data structure.
Solution Approach 2:
The ranking system combines multiple factors (user count, annotation frequency, recency) into a composite popularity score, similar to how composite materials combine different properties. This composite approach enables accurate popularity measurement by integrating multiple dimensions of user engagement rather than relying on a single metric.
2Reliability
If all annotations are weighted equally regardless of time, then the ranking system is simple to maintain, but it emphasizes old annotations over recent ones
Solution Approach 1:
The patent implements periodic action by applying exponential decay to annotation contributions over time. This means that annotations lose weight systematically as time progresses, with recent annotations having greater impact. This time-based periodic weighting ensures the ranking reliably reflects current popularity trends while maintaining a systematic approach to decay.
3Measurement precision
If the system considers only the number of annotations per user, then the calculation is straightforward, but it fails to capture the diversity of user engagement
Solution Approach 1:
The patent segments the user engagement metric into two distinct components: the number of unique users and the average frequency of annotations per user. This segmentation allows the system to capture both the breadth (how many users) and depth (how actively they annotate) of engagement, providing a more accurate popularity measurement while keeping calculations manageable.
Data Source
AI summary
Techniques for ranking electronic content items include analyzing annotations made in the content items. Specifically, the number of annotations and the number of people making the annotations can be analyzed to produce a popularity ranking of different content items.