Results 1 - 10 of 523,984
Evaluating collaborative filtering recommender systems
by Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, John T. Riedl - ACM TRANSACTIONS ON INFORMATION SYSTEMS , 2004
Property Rights and the Nature of the Firm
by Oliver Hart, John Moore - JOURNAL OF POLITICAL ECONOMY , 1990
CORE: A Collaborative Reputation mechanism to enforce node cooperation
by Pietro Michiardi, Refik Molva - in Mobile Ad Hoc Networks. Communication and Multimedia Security , 2002
"... hoc Networks. ..."
hoc Networks.
The Ontolingua Server: a Tool for Collaborative Ontology Construction
by Adam Farquhar, Richard Fikes, James Rice - International Journal of Human-Computer Studies , 1996
"... Reusable ontologies are becoming increasingly important for tasks such as information integration, knowledge-level interoperation, and knowledgebase development. We have developed a set of tools and services to support the process of achieving consensus on common shared ontologies by geographically ..."
Reusable ontologies are becoming increasingly important for tasks such as information integration, knowledge-level interoperation, and knowledgebase development. We have developed a set of tools and services to support the process of achieving consensus on common shared ontologies by geographically distributed groups. These tools make use of the worldwide web to enable wide access and provide users with the ability to publish, browse, create, and edit ontologies stored on an ontology server. Users can quickly assemble a new ontology from a library of modules. We discuss how our system was constructed, how it exploits existing protocols and browsing tools, and our experience supporting hundreds of users. We describe applications using our tools to achieve consensus on ontologies and to integrate information. The Ontolingua Server may be accessed through the URL http://ontolingua.stanford.edu/
Crowds: Anonymity for Web Transactions
by Michael K. Reiter, Aviel D. Rubin - ACM Transactions on Information and System Security , 1997
"... this paper we introduce a system called Crowds for protecting users' anonymity on the worldwide -web. Crowds, named for the notion of "blending into a crowd", operates by grouping users into a large and geographically diverse group (crowd) that collectively issues requests on behalf o ..."
of its members. Web servers are unable to learn the true source of a request because it is equally likely to have originated from any member of the crowd, and even collaborating crowd members cannot distinguish the originator of a request from a member who is merely forwarding the request on behalf
Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization
by Benjamin Recht, Maryam Fazel, Pablo A. Parrilo , 2007
"... The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and control, Euclidean embedding, and collaborative ..."
, and collaborative filtering. Although specific instances can often be solved with specialized algorithms, the general affine rank minimization problem is NP-hard, because it contains vector cardinality minimization as a special case. In this paper, we show that if a certain restricted isometry property holds
Agile Application-Aware Adaptation for Mobility
by Brian D. Noble, M. Satyanarayanan, Dushyanth Narayanan, James Eric Tilton, Jason Flinn, Kevin R. Walker - SOSP-16 , 1997
"... In this paper we show that application-aware adaptation, a collaborative partnership between the operating system and applications, offers the most general and effective approach to mobile information access. We describe the design of Odyssey, a prototype implementing this approach, and show how it ..."
In this paper we show that application-aware adaptation, a collaborative partnership between the operating system and applications, offers the most general and effective approach to mobile information access. We describe the design of Odyssey, a prototype implementing this approach, and show how
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
by Gediminas Adomavicius, Alexander Tuzhilin - IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2005
"... This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes vario ..."
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes
Towards a Standard Upper Ontology
by Ian Niles, Adam Pease , 2001
"... The Suggested Upper Merged Ontology (SUMO) is an upper level ontology that has been proposed as a starter document for The Standard Upper Ontology Working Group, an IEEE-sanctioned working group of collaborators from the fields of engineering, philosophy, and information science. The SUMO provides d ..."
The Suggested Upper Merged Ontology (SUMO) is an upper level ontology that has been proposed as a starter document for The Standard Upper Ontology Working Group, an IEEE-sanctioned working group of collaborators from the fields of engineering, philosophy, and information science. The SUMO provides
An Efficient Boosting Algorithm for Combining Preferences
by Raj Dharmarajan Iyer , Jr. , 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. Here, we present results comparing Rank