RecomSyst

Referral Web:

1Note that by “social network” we explicitly include groups of people linkedby professional activities. We do not use the word “social” in the morelimited sense of “recreational.’’ Rec

omm

ende

r Sy

stem

sPart of the success of social networks can be attrib-

uted to the “six degrees of separation’’ phenomena thatmeans the distance between any two individuals interms of direct personal relationships is relativelysmall. An equally important factor is there are limitsto the amount and kinds of information a person isable or willing to make available to the public atlarge. For example, an expert in a particular field isalmost certainly unable to write down all he knowsabout the topic, and is likely to be unwilling to makeletters of recommendation he or she has written forvarious people publicly available. Thus, searching fora piece of information in this situation becomes amatter of searching the social network for an experton the topic together with a chain of personal referralsfrom the searcher to the expert. The referral chain

serves two key functions: It provides a reason for theexpert to agree to respond to the requester by makingtheir relationship explicit (for example, they have amutual collaborator), and it provides a criteria for thesearcher to use in evaluating the trustworthiness ofthe expert.

Nonetheless, manually searching for a referralchain can be a frustrating and time-consuming task.One is faced with the trade-off of contacting a largenumber of individuals at each step, and thus strainingboth the time and goodwill of the possible respon-dents, or of contacting a smaller, more focused set, andbeing more likely to fail to locate an appropriateexpert. In response to these problems we are buildingReferralWeb, an interactive system for reconstructing,visualizing, and searching social networks on theWorld-Wide Web. Simulation experiments we ranbefore we began construction of ReferralWeb showedthat automatically generated referrals can be highly

COMMUNICATIONS OF THE ACM March 1997/Vol. 40, No. 3 63

NUMEROUS STUDIES HAVE SHOWN THAT ONE OF THE MOST EFFEC-

tive channels for disseminating of information and expertise

within an organization is its informal network of collabora-

tors, colleagues, and friends [1, 4, 7]. Indeed, the social network1 is as

least as important as the official organizational structure for tasks rang-

ing from immediate, local problem-solving (for example, fixing a piece

of equipment), to primary work functions, such as creating project teams.

Combining Social Networksand Collaborative Filtering

An interactive system for restructuring, visualizing, and searching social networks on the Web.

Henry Kautz, Bart Selman,

and Mehul Shah

64 March 1997/Vol. 40, No. 3 COMMUNICATIONS OF THE ACM

successful in locating experts in a large network [3].The higher degree of responsiveness of an automatedsystem can be effectively traded-off for the (poten-tially) lower accuracy of individual automatic referralsover those generated by a person.

Reconstructing and QueryingSocial NetworksA social network is modeled by a graph, where thenodes represent individuals, and an edge betweennodes indicates that a direct relationship between theindividuals has been discovered. There are many pos-sible sources for determining direct relationships. Atone extreme, which we reject astoo burdensome, users could berequired to enter lists of closecolleagues. Analyzing of emaillogs provides a rich source ofrelationships, as shown bySchwartz and Wood [5]. In fact,the initial version of our systemderived its network by analyzingmail archives [3]. However, theuse of such information raisesconcerns of privacy and securitythat are hard to allay.

The current ReferralWeb sys-tem uses the co-occurrence ofnames in close proximity in anydocuments publicly available onthe Web as evidence of a directrelationship. Such sourcesinclude:

• Links found on home pages• Lists of co-authors in technical papers and cita-

tions of papers• Exchanges between individuals recorded in net-

news archives• Organization charts (such as for university depart-

ments)

The network model is constructed incrementally.When a user first registers with the system, it uses ageneral search engine to retrieve Web documents thatmention him or her. The names of other individualsare extracted from the documents. This can be donewith high degree of accuracy (better than 90%), usingtechniques such as those described in [6]. This processis applied recursively for one or two levels, and theresult merged into the global network model.

The network is then used to guide the search forpeople or documents in response to user queries. Mostsimply, a person may ask to find the chain between

himself/herself and a named individual (“What is myrelationship to Marvin Minsky?’’). To search for anexpert, the user may specify both a topic and an effec-tive social radius: for example, a query might be,“What colleagues of mine, or colleagues of colleaguesof mine, know about simulated annealing?’’ Anotherkind of query takes advantage of a designated, knownexpert to control the search. For instance, one mightask to “List documents on the topic ‘annealing’ bypeople close to Scott Kirkpatrick.’’

It is important to emphasize that ReferralWebdoes not replace generic search engines such asAltaVista, but instead uses the social network to

make their search more focusedand effective. For example, inthe previous scenario, therequirement of proximity to(the computer scientist) Kirk-patrick helps disambiguate thequery between the computerscience use of the term “anneal-ing’’ and the use of the sameterm in metallurgy. The socialnetwork also prioritizes theanswers, in that the userretrieves hits on people that areclosest to him or herself, ratherthan simply a long list of hun-dreds of names or documents.

ReferralWeb has a number offeatures that sets it apart frommany other collaborative filter-ing and recommender projects.Among those are the following:

• ReferralWeb attempts to uncover existing socialnetworks, rather than provide a tool for creatingnew communities. While new communities maybe appropriate for recreational uses of the Web, weemphasize helping individuals make more effectiveuse of their large, existing networks of professionalcolleagues.

• While recommender systems are often designed toprovide anonymous recommendations, ReferralWebis based on providing referrals via chains of namedindividuals. This is critical because not all sourcesof information are equally desirable. For example, auser may choose to search for referrals to peoplewho are closely associated with some known,trusted expert.

• Some recommender systems require the user tomanually enter a personal profile of interests, pref-erences, or expertise. Recommendations are gener-ated by matching profiles that exist within the

The ReferralWeb system is part of our effortto develop agent-basedprograms thataddress practical communication needs

system. By contrast, ReferralWeb primarily buildsits model of its users’ social network by data min-ing public documents found on the Web. Thismodel includes many more individuals than thosewho explicitly register with the service.

• Users of ReferralWeb are not limited to any set oftopic areas determined in advance. ReferralWebuses a general full Web indexing engine (currentlyAltaVista) to match individuals to topic areas.

The ReferralWeb system is part of our effort todevelop agent-based programs that address practicalcommunication needs [2]. We have developed severalinternal prototypes of thesystem, and are currentlybuilding a version that willbe made accessible exter-nally on the Web.

Finally, the ReferralWebsystem expands usersawareness of their existingcommunities. Typically auser is only aware of a por-tion of the social networkto which he or she belongs.By instantiating the largercommunity, the user candiscover connections topeople and informationthat would otherwise layhidden over the horizon.

REFERENCES1. Granovetter, M. Strength of weak

ties. Amer. J. Sociology 78 (1973),1360–1380.

2. Kautz, H., Selman, B., and Coen, M.Bottom-up design of software agents.Commun. ACM 37, 7 (1994)143–146.

3. Kautz, H., Selman, B., and Milewski,A. Agent amplified communication.In Proceedings of AAAI-96 (Portland,Oreg.) MIT Press, Cambridge, Mass.1996, 3–9.

4. Kraut, R., Galegher, J. and Edigo C.Intellectual Teamwork: Social and Tech-nological Bases for Cooperative Work.Lawrence Erlbaum, Hillsdale, NJ,1990.

5. Schwartz, M. F. and Wood, D. C. M.Discovering shared interests usinggraph analysis. Comm. ACM 36, 8(1993), 78–89.

6. Sundheim, B. and Grishman, R., Eds.In Proceedings of the Sixth Message Un-derstanding Conference (MUC-6). Mor-gan Kaufmann, San Francisco, Calif.,1995.

7. Wasserman, S. and Galaskiewicz, J.,Eds. Advances in Social Network Analy-sis. Sage, Thousand Oaks, Calif.,1994.

Henry Kautz (kautz@research.att.com) is head of the Principlesof Artificial Intelligence Research Department at AT&T Labs,Florham Park, N.J. Bart Selman (selman@research.att.com) is a research scientist atAT&T Labs, Florham Park, N.J.Mehul Shah (shah@mit.edu) is a Masters student at M.I.T inthe AT&T/M.I.T VI-A co-op program.

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