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The Internet has created vast new opportunities to interact with strangers. The interactions can be fun, informative, even profitable. But they also involve risks. Is the advice from a self-proclaimed expert at expertcentral.com reliable? Will an unknown dot-com site or eBay seller ship with appropriate packaging, and will the product be as described? Before the Internet, such questions were answered, in part, through reputations. Vendors provided references, Better Business Bureaus tallied complaints, and past personal experience and person-to-person gossip told you whom you could rely upon and whom you could not. And a businessman’s standing in the community, e.g., his role at church, served as a valuable hostage. Internet services operate on a vastly larger scale than Main Street and permit virtually anonymous interactions. Nevertheless, reputation systems are playing a major role. Systems are emerging that respect anonymity and operate on the Internet’s scale. A reputation system collects, distributes, and aggregates feedback about participants’ past behavior. Though few of the producers or consumers of the ratings know each other, these systems help people decide whom to trust, encourage trustworthy behavior, and deter participation by those who are unskilled or dishonest. For example, consider eBay, the largest person-to-person online auction site, with more than 4 million auctions open at a time. eBay offers no warranty for its auctions; it only serves as a listing service while the buyers and the sellers assume all the risks associated with transactions. There are fraudulent transactions to be sure. Nonetheless, the overall rate of successful transactions remains astonishingly high for a market as “ripe with the possibility of largescale fraud and deceit” as is eBay [ref. Kollock]. eBay attributes its high rate of successful transactions to its reputation system, the Feedback Forum. After a transaction is completed, the buyer and seller have the opportunity to rate each other (1, 0, or -1) and leave comments (“Good transaction. Nice person to do business with! Would highly recommend.”). Each participant has his running total of feedback points attached visibly to
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Stored only when both papers are in the corpus — 196 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.