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System for Collecting, Aggregating, and Managing Community-Sourced Ratings Data for Entities Which Evolve OverTime Disclosure Number: IPCOM000207114D
Publication Date: 2011-May-17
Document File: 5 page(s) / 62K

Publishing Venue

The Prior Art Database


Disclosed is a system for facilitating ratings of ever-evolving resources in a way that provides more meaningful results based upon specified points-in-time.

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System for Collecting, Aggregating, and Managing Community-Sourced Ratings Data for Entities Which Evolve OverTime

Traditional rating systems are not well suited for use in an environment where those resources are subject to change (e.g., a set of published content that is subject to revisions, or a service that is provided that differs in quality based upon any number of factors). The reason for this is the fact that a user-supplied rating is representative of a point-in-time of the referenced resource, and can become outdated once the nature of that resource changes. At this point, there is no guarantee that the current rating is still valid.

For resources that are more static in nature (such as information that is only published once and never changes), there is much less chance of ratings data becoming obsolete due to the underlying nature of the resource changing. For example, a user's satisfaction about the quality and accuracy of published information that does not change is probably going to still be valid in the future.

What is needed is a system that will allow users to change their rating of the resource while preserving the old value into a set of historical data that is able to be queried. This would have the advantage of only including the most up to date average for any given resource, while providing a means for the historical data to be used in parameterized queries that will give the average satisfaction of the resource quality for any given point in time or date range.

In a more dynamic and fluid environment such as a wiki, the underlying content is much more likely to have multiple edits applied to it as the content evolves, and the ratings for past revisions might not necessarily be applicable to present/future revisions of the content. In this type of scenario, what value do ratings bring to the table and how should their life cycle be managed? Do we take the ratings data at face value with no respect to the time line of revisions? Or should ratings be a more fluid data set that is respective to time (for example, only ratings that were acquired between revision X and revision Y are included in the aggregation for said revision)?

The disclosed system solves this problem in the following ways:
• Users are allowed to change their ratings of the content, and the most recently supplied value is the one that will be included in the normal/current computed average rating.

• When a user changes an existing rating, the old value is archived in a ratings history table that preserves the timestamp of when the rating was originally submitted.

• Through parameterized queries of the ratings data, the system provides the ability to look back into any point in time to compute the average rating for any given version of the content (where the content system's own knowledge of the date/time the revision took place is used to parameterize the query into the historical data).


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This invention works by offerin...