Building Web Reputation Systems- P8

Building Web Reputation Systems- P8:Today’s Web is the product of over a billion hands and minds. Around the clock andaround the globe, people are pumping out contributions small and large: full-lengthfeatures on Vimeo, video shorts on YouTube, comments on Blogger, discussions onYahoo! Groups, and tagged-and-titled Del.icio.us bookmarks. User-generated contentand robust crowd participation have become the hallmarks of Web 2.0.
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Building Web Reputation Systems- P8 copying and pasting an HTML snippet that the application provides. Flickr’s pat- ent doesn’t specifically say that these two actions are treated similarly, but it seems reasonable to do so.Generally, four things determine a Flickr photo’s interestingness (represented by thefour parallel paths in Figure 4-9): the viewer activity score, which represents the effectof viewers taking a specific action on a photo; tag relatedness, which represents a tag’ssimilarity to others associated with other tagged photos; the negative feedback adjust-ment, which reflects reasons to downgrade or disqualify the tag; and group weighting,which has an early positive effect on reputation with the first few events. 5. The events coming into the Karma Weighting process are assumed to have a nor- malized value of 0.5, because the process is likely to increase it. The process reads the interesting-photographer karma of the user taking the action (not the person who owns the photo) and increases the viewer activity value by some weighting amount before passing it onto the next process. As a simple example, we’ll suggest that the increase in value will be a maximum of 0.25—with no effect for a viewer with no karma and 0.25 for a hypothetical awesome user whose every photo is beloved by one and all. The resulting score will be in the range 0.5 to 0.75. We assume that this interim value is not stored in a reputation statement for perform- ance reasons. 6. Next, the Relationship Weighting process takes the input score (in the range of 0.5 to 0.75) and determines the relationship strength of the viewer to the photographer. The patent indicates that a stronger relationship should grant a higher weight to any viewer activity. Again, for our simple example, we’ll add up to 0.25 for a mutual first-degree relationship between the users. Lower values can be added for one-way (follower) relationships or even relationships as members of the same Flickr groups. The result is now in the range of 0.5 to 1.0 and is ready to be added into the historical contributions for this photo. 7. The Viewer Activity Score is a simple accumulator and custom denormalizer that sums up all the normalized event scores that have been weighted. In our example, they arrive in the range of 0.5 to 1.0. It seems likely that this score is the primary basis for interestingness. The patent indicates that each sum is marked with a timestamp to track changes in viewer activity score over time. The sum is then denormalized against the available range, from 0.5 to the maxi- mum known viewer activity score, to produce an output from 0.0 to 1.0, which represents the normalized accumulated score stored in the reputation system so that it can be used to recalculate photo interestingness as needed. 8. Unlike most of the reputation messages we’ve considered so far, the incoming message to the tagging process path does not include any numeric value at all; it contains only the text tag that the viewer is adding to the photo. The tag is first subjected to the Tag Blacklist process, a simple evaluator that checks the tag against86 | Chapter 4: Common Reputation Models a list of forbidden words. If the flow is terminated for this event, there is no con- tribution to photo interestingness for this tag. Separately, it seems likely that Flickr would want a tag on the list of forbidden words to have a negative, penalizing effect on the karma score for the person who added it. Otherwise, the tag is considered worthy of further reputation consideration and is sent on to the Tag Relatedness process. Only if the tag was on the list of forbidden words is it likely that any record of this process would be saved for future reference. 9. The nonblacklisted tag then undergoes the Tag Relatedness process, which is a custom computation of reputation based on cluster analysis described in the patent in this way (from Flickr’s U.S. Patent Application No. 2006/0242139 A1): [0032] As part of the relatedness computation, the statistics engine may employ a statistical clustering analysis known in the art to determine the statistical proximity between metadata (e.g., tags), and to group the metadata and associated media objects according to corresponding cluster. For example, out of 10,000 images tag- ged with the word “Vancouver,” one statistical cluster within a threshold proximity level may include images also tagged with “Canada” and “British Columbia.” An- other statistical cluster within the threshold proximity may instead be tagged with “Washington” and “space needle” along with “Vancouver.” Clustering analysis allows the statistics engine ...