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Building Web Reputation Systems- P19

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The cycle then repeats, if you’re lucky.. It’s quite possible that—even during the beta period of your deployment—you’re no- ticing some strange effects starting to take hold. Why might this be? Technology essayist Paul Graham posits that it may be the users of the service, and their motiva- tional mindset, that explain it: “Delicious users are collectors, and a list...

Building Web Reputation Systems- P20

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The team devised this plan for the new model: a reputation model would sit between the two existing systems—a report mechanism that permitted any user on Yahoo! An- swers to flag any other user’s contribution and the (human) customer care system that acted on those reports. Customer care could be removed from the loop—in most cases—by shifting the content removal...

Building Web Reputation Systems- P21

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The inputs and calculations for this process were the same as in the third iteration of the model—the process remained a repository for all confirmed and nonap- pealed user content violations. The only difference was that every time the system executed the process and updated AbusiveContent karma, it now sent an additional message to the Abuse Reporter Bootstrap process.. This...

Building Web Reputation Systems- P22

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Look at the history of Twitter.com’s Fail Whale, a direct result of the requirement for a dynamic custom display of tweets for each and every user. This book won’t cover the many forms of dynamic systems in any depth, as many of the algorithms are well cov- ered in academic literature. At the point where your reputation system grows large...

Building Web Reputation Systems- P23

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message to the Yahoo! Profiles karma model without knowing the address for the dispatcher. it can just send a message to the one for its own framework and know that the message will get relayed to the appropriate servers.. Note that a registration service, such as the one described for the dispatch consumer, is required to support this functionality.. Since...

Building Web Reputation Systems- P24

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abuse reporters on Yahoo! Answers, 257 authors on Yahoo! Answers, 260 caveats, 177. eBay seller feedback karma, 78–82 generating inferred karma, 159–161 inferred karma in Yahoo! Answers, 263 negative public karma, 161. levels in reputation display, 185–189 named levels, 188. invisible reputation framework, 288 optimistic versus request-reply, 286 Yahoo! Reputation Platform, 292 messaging dispatcher, Yahoo! Reputation. named levels in reputation...

Hướng dẫn sử dụng μVision3

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Ch ươ ng 1 . Ch ươ ng 2. Ch ươ ng 3. Ch ươ ng 4 .Ti n ích trong μVision3 ệ ...42.. Ch ươ ng 5. Ki m tra ch ể ươ ng trình v i Trình g r i ớ ỡ ố μvision3 ...56.. Ch ươ ng 6..L nh Debug ệ ...98.. Ch ươ ng 7. Ch...

Học lập trình Flash

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Dòng đầu tiên xác định những đoạn code sau đó được kích hoạt khi mà user nhấn vào nút. hàm on(press) chỉ có thể sự dụng trong vào trong nút mà thôi. Ngoài ra bạn có thể dùng on(release) nếu như bạn muốn kích hoạt đoạn mã trên sau khi user nhấn nút, và buông tay.. ờ đầu và cuối ...

LẬP TRÌNH FLASH

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Dòng đầu tiên xác định những đoạn code sau đó được kích hoạt khi mà user nhấn vào nút. hàm on(press) chỉ có thể sự dụng trong vào trong nút mà thôi. Ngoài ra bạn có thể dùng on(release) nếu như bạn muốn kích hoạt đoạn mã trên sau khi user nhấn nút, và buông tay.. ờ đầu và cuối ...