Chapters 5 and 7 Ready for Review
We've been busy boys over here on BuildingReputation.com, though—unless you were paying careful attention—you might not have noticed. We've fallen victim to something that strikes all authors (we suspect) and have been so busy drafting, outlining, writing & revising that we've had a hard time keeping up with this site, and promoting the ongoing progress on the book.
The good news is, we're more than half-way to Draft Complete status. (Check out that sidebar on the wiki. 7 chapters down, only 5 to go!) So we have a plan for returning our attentions to this site, and growing the audience here that will continue to feed insight and course-corrections into the remaining chapters. (That's the theory anyway. Have we mentioned that this Unbook stuff is harder than we thought it would be?)
The really good news is this: Chapters Five and Seven are now draft-complete and ready for your review. A bit about each…
Chapter 5: Common Reputation Models
Chapter 5 is a pivotal point in the book, and represents something of a transition from the theoretical & abstract visual grammar of the early chapters to a real-world, applied demonstration of that grammar.
We look at some 'common' reputation models (tho', it's a recurring argument of ours throughout the book that there may not truly be any effective common 'off the rack' reputation models. All of these require some modification and combination to suit your specific context.) Here, for example, 'Robust Karma' shows how to get the right mix of participant quality and activity in your karma model…
When needed, the Quality Karma and Participation Karma can be mixed into one score representing the combined value of this user's contributions. Each application decides how much weight each component gets in the final calculation. Often these Quality Karma are not displayed to users but only used for internal ranking for highlighting or attention and as search ranking influence factors, see Chap_5-Keep_Your_Barn_Door_Closed later this chapter for common reasons for this secrecy. But even when displayed, robust karma has the advantage of encouraging users to both the best stuff (as evaluated by their peers) and to do it often.
When negative factors are mixed into robust karma, it is particularly useful for customer care staff - both to highlight users that have become abusive or are decreasing content value, and to potentially provide an increased level of service in the case of a service event. This karma helps find the best of the best and the worst of the worst.
Figure_5-V: Robust Karma combines multiple other Karma scores, usually Qualitative and Quantitative for simplicities sake at the cost of obscuring detail.
Then we move on to something we know you're gonna like: we explore some well-known and high-profile reputation models that lie behind a couple of the Web's powerhouses. Ebay Seller Reputation is perhaps the most-observed (and emulated) reputation system going today and if you anticipate designing any type of marketplace or trust-based karma system, it is well worth a read to understand exactly how reputation works on Ebay.
Similarly, we do a deep-dive into how Flickr's Interestingness model ensures a stream of high-quality and reliably enjoyable photos to their Explore section of that site. This stuff is required reading for social software architects & designers. Trust us.
Chapter 7: Objects, Inputs, Scope, and Mechanism
This is the practitioners' chapter. In Chapter 6, we've asked a lot of the foundational questions about your intended reputation program: What do you hope to achieve? What behaviors are you trying to encourage? Discourage? How will you measure your progress?
In Chapter 7, with those goals firmly in mind, we show you how to start architecting your reputation system. We discuss how to identify the objects in your application that should accrue reputation, and give some guidance on determining which reputation inputs you should pay attention to. If you're curious about "Thumbs or Stars?" or "How is a 'Favorite' different from a 'Like'?" then we try to provide some guidance in this chapter.
And, as we're careful to remind, reputation takes place within a context, and an important facet of that context is its scope: how wide-ranging (or specific) should earned reputations be to the 'location' and context that they're earned in? Then, finally, we suggest a number of ordering mechanisms that show you how Objects, Inputs & Scope all combine to serve those goals that we established in Chapter 6.
Chapter 7 is a doozy of a chapter, folks. It almost begs to be it's own book. But, please, don't be intimidated—there's a lot of good raw material in there, but we still really do need your help and criticism to tease a great chapter out of it. Your comments are always welcome.