Referral Web

Kautz, Henry; Selman, Bart; Shah, Mehul A.

doi:10.1145/245108.245123

Cited by 656 publications

(267 citation statements)

References 4 publications

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“…Applying the same methods to the Semantic Web's web of trust may prove fruitful in identifying useful contributors, highly respected entities, etc. Also in a similar vein is the ReferralWeb project, which mines multiple sources to discover networks of trust among users [22]. Also interesting is collaborative filtering [30], in which a user's belief is computed from the beliefs of users she is similar to.…”

Section: Related Workmentioning

confidence: 99%

Trust Management for the Semantic Web

Richardson

Agrawal

Domingos

2003

Lecture Notes in Computer Science

697

385

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Abstract. Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate "trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.

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Section: Related Workmentioning

confidence: 99%

Trust Management for the Semantic Web

Richardson

Agrawal

Domingos

2003

Lecture Notes in Computer Science

697

385

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“…For example the Referral Web project at AT&T [13], focused on the creation of social networks from the co-existence of names on the Web and use these models to locate experts and referral paths. However, it is not possible to infer the type of relations with this approach.…”

Section: Related Workmentioning

confidence: 99%

“…Information about the user, for example, can help personalising the expert search to those with similar roles or job categories as the user, or to those that are within short social paths to the user to encourage response [13]. The hierarchy of topics can be used to narrow down or broaden a query if too many or too few results are found respectively.…”

Section: Design Issuesmentioning

confidence: 99%

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Design Issues for Agent-Based Resource Locator Systems

Wills

Alani

Ashri

et al. 2002

Practical Aspects of Knowledge Management

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Abstract. While knowledge is viewed by many as an asset, it is often difficult to locate particular items within a large electronic corpus. This paper presents an agent based framework for the location of resources to resolve a specific query, and considers the associated design issue. Aspects of the work presented complements current research into both expertise finders and recommender systems. The essential issues for the proposed design are scalability, together with the ability to learn and adapt to changing resources. As knowledge is often implicit within electronic resources, and therefore difficult to locate, we have proposed the use of ontologies, to extract the semantics and infer meaning to obtain the results required. We explore the use of communities of practice, applying ontology-based networks, and e-mail message exchanges to aid the resource discovery process.

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“…Referral Chains use word co-occurrences to build an informal network and uses the network to enhance web search [6]. Yet the question seems to be how to apply it since social connections are not necessarily the same as trusted connections, and may be tenuous at best.…”

Section: Related Workmentioning

confidence: 99%

Open Domain Recommendation: Social Networks and Collaborative Filtering

Tyler

Zhang

Advanced Data Mining and Applications

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Abstract. Commercial enterprises employee data mining techniques to recommend products to their customers. This recommendation is based on similar purchasing histories, as well as similarities between users and similarities between products. It typically involves analyzing a specific domain such as movies or books to make predictions as to which user is likely to want which product, increasing sales and providing users with desirable content. But when the domain isn't as specific, or the data becomes too sparse to draw similarities between users, prediction becomes much more difficult. One avenue to find similarity in users is to utilize the user provided social connections, under the assumption they share some like-mindedness. We propose using social network data, along with the contextual information to enhance product recommendations in a sparse context. We show that when a social network can be applied, it is a strong indicator of user preference for product recommendations, having high precision at the cost of recall. Although the sparseness of the data may mean that the social network is not always applicable, we present a solution to utilize the network in these cases.

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Referral Web

Cited by 656 publications

References 4 publications

Trust Management for the Semantic Web

Trust Management for the Semantic Web

Design Issues for Agent-Based Resource Locator Systems

Open Domain Recommendation: Social Networks and Collaborative Filtering

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