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4,539
Exploiting the Diversity of User Preferences for Recommendation
"... Diversity as a quality dimension for Recommender Systems has been receiving increasing attention in the last few years. This has been paralleled by an intense strand of research on diversity in search tasks, and in fact converging views on diversity theories and techniques from Information Retrieval ..."
Abstract
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Cited by 3 (0 self)
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Retrieval and Recommender Systems have been put forward in recent work. In this paper we research diversity not only as a target property for a recommender system, but as an element in the input data, within and between user behaviors, that a recommender system can leverage to enhance the quality of its
Fab: Content-based, collaborative recommendation
- Communications of the ACM
, 1997
"... Fab is a recommendation system designed to help users sift through the enormous amount of information available in the World Wide Web. Operational since Dec. 1994, this system combines the content-based and collaborative methods of recommendation in a way that exploits the advantages of the two appr ..."
Abstract
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Cited by 682 (0 self)
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Fab is a recommendation system designed to help users sift through the enormous amount of information available in the World Wide Web. Operational since Dec. 1994, this system combines the content-based and collaborative methods of recommendation in a way that exploits the advantages of the two
Opportunistic Beamforming Using Dumb Antennas
- IEEE Transactions on Information Theory
, 2002
"... Multiuser diversity is a form of diversity inherent in a wireless network, provided by independent time-varying channels across the different users. The diversity benefit is exploited by tracking the channel fluctuations of the users and scheduling transmissions to users when their instantaneous cha ..."
Abstract
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Cited by 811 (1 self)
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Multiuser diversity is a form of diversity inherent in a wireless network, provided by independent time-varying channels across the different users. The diversity benefit is exploited by tracking the channel fluctuations of the users and scheduling transmissions to users when their instantaneous
Mobility increases the capacity of ad-hoc wireless networks
- IEEE/ACM TRANSACTIONS ON NETWORKING
, 2002
"... The capacity of ad-hoc wireless networks is constrained by the mutual interference of concurrent transmissions between nodes. We study a model of an ad-hoc network where n nodes communicate in random source-destination pairs. These nodes are assumed to be mobile. We examine the per-session throughpu ..."
Abstract
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Cited by 1220 (5 self)
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-session throughput for applications with loose delay constraints, such that the topology changes over the time-scale of packet delivery. Under this assumption, the per-user throughput can increase dramatically when nodes are mobile rather than fixed. This improvement can be achieved by exploiting node mobility as a
Relational Databases for Querying XML Documents: Limitations and Opportunities
, 1999
"... XML is fast emerging as the dominant standard for representing data in the World Wide Web. Sophisticated query engines that allow users to effectively tap the data stored in XML documents will be crucial to exploiting the full power of XML. While there has been a great deal of activity recently prop ..."
Abstract
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Cited by 478 (9 self)
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XML is fast emerging as the dominant standard for representing data in the World Wide Web. Sophisticated query engines that allow users to effectively tap the data stored in XML documents will be crucial to exploiting the full power of XML. While there has been a great deal of activity recently
Factorization meets the neighborhood: a multifaceted collaborative filtering model
- In Proc. of the 14th ACM SIGKDD conference
, 2008
"... Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent f ..."
Abstract
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Cited by 424 (12 self)
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Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent
Recommendation as Classification: Using Social and Content-Based Information in Recommendation
- In Proceedings of the Fifteenth National Conference on Artificial Intelligence
, 1998
"... Recommendation systems make suggestions about artifacts to a user. For instance, they may predict whether a user would be interested in seeing a particular movie. Social recomendation methods collect ratings of artifacts from many individuals and use nearest-neighbor techniques to make recommendatio ..."
Abstract
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Cited by 342 (8 self)
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that is able to use both ratings information and other forms of information about each artifact in predicting user preferences. We show that our method outperforms an existing social-filtering method in the domain of movie recommendations on a dataset of more than 45,000 movie ratings collected from a
Content-Based Book Recommending Using Learning for Text Categorization
- IN PROCEEDINGS OF THE FIFTH ACM CONFERENCE ON DIGITAL LIBRARIES
, 1999
"... Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. ..."
Abstract
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Cited by 334 (8 self)
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Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences
Learning Collaborative Information Filters
- In Proc. 15th International Conf. on Machine Learning
, 1998
"... Predicting items a user would like on the basis of other users’ ratings for these items has become a well-established strategy adopted by many recommendation services on the Internet. Although this can be seen as a classification problem, algo-rithms proposed thus far do not draw on results from the ..."
Abstract
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Cited by 354 (4 self)
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Predicting items a user would like on the basis of other users’ ratings for these items has become a well-established strategy adopted by many recommendation services on the Internet. Although this can be seen as a classification problem, algo-rithms proposed thus far do not draw on results from
CP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements
- JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 2004
"... Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional dep ..."
Abstract
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Cited by 317 (4 self)
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Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional
Results 1 - 10
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4,539
