Recom Syst Referral

Part of the success of social networks can be attributed to the “six degrees of separation’’ phenomena that means the distance between any two individuals in terms of direct personal relationships is relatively small. An equally important factor is there are limits to the amount and kinds of information a person is able or willing to make available to the public at large. For example, an expert in a particular field is almost certainly unable to write down all he knows about the topic, and is likely to be unwilling to make letters of recommendation he or she has written for various people publicly available. Thus, searching for a piece of information in this situation becomes a matter of searching the social network for an expert on the topic together with a chain of personal referrals from the searcher to the expert. The referral chain serves two key functions: It provides a reason for the expert to agree to respond to the requester by making their relationship explicit (for example, they have a mutual collaborator), and it provides a criteria for the searcher to use in evaluating the trustworthiness of the expert. Nonetheless, manually searching for a referral chain can be a frustrating and time-consuming task. One is faced with the trade-off of contacting a large number of individuals at each step, and thus straining both the time and goodwill of the possible respondents, or of contacting a smaller, more focused set, and being more likely to fail to locate an appropriate expert. In response to these problems we are building ReferralWeb, an interactive system for reconstructing, visualizing, and searching social networks on the World-Wide Web. Simulation experiments we ran before we began construction of ReferralWeb showed that automatically generated referrals can be highly