Results 1 - 10
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1,500
Empirical Analysis of Predictive Algorithm for Collaborative Filtering
- Proceedings of the 14 th Conference on Uncertainty in Artificial Intelligence
, 1998
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Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
- 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 ..."
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Cited by 1420 (21 self)
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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 various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
Item-based Collaborative Filtering Recommendation Algorithms
- PROC. 10TH INTERNATIONAL CONFERENCE ON THE WORLD WIDE WEB
, 2001
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Social Information Filtering: Algorithms for Automating "Word of Mouth"
, 1995
"... This paper describes a technique for making personalized recommendations from any type of database to a user based on similarities between the interest profile of that user and those of other users. In particular, we discuss the implementation of a networked system called Ringo, which makes personal ..."
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Cited by 1145 (21 self)
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This paper describes a technique for making personalized recommendations from any type of database to a user based on similarities between the interest profile of that user and those of other users. In particular, we discuss the implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists. Ringo's database of users and artists grows dynamically as more people use the system and enter more information. Four different algorithms for making recommendations by using social information filtering were tested and compared. We present quantitative and qualitative results obtained from the use of Ringo by more than 2000 people.
Evaluating collaborative filtering recommender systems
- ACM TRANSACTIONS ON INFORMATION SYSTEMS
, 2004
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Grouplens: Applying collaborative filtering to usenet news
- COMMUNICATIONS OF THE ACM
, 1997
"... ... a collaborative filtering system for Usenet news—a high-volume, high-turnover discussion list service on the Internet. Usenet newsgroups—the individual discussion lists—may carry hundreds of messages each day. While in theory the newsgroup organization allows readers to select the content that m ..."
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Cited by 787 (16 self)
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... a collaborative filtering system for Usenet news—a high-volume, high-turnover discussion list service on the Internet. Usenet newsgroups—the individual discussion lists—may carry hundreds of messages each day. While in theory the newsgroup organization allows readers to select the content that most interests them, in practice most newsgroups carry a wide enough spread of messages to make most individuals consider Usenet news to be a high noise information resource. Furthermore, each user values a different set of messages. Both taste and prior knowledge are major factors in evaluating news articles. For example, readers of the rec.humor newsgroup, a group designed for jokes and other humorous postings, value articles based on whether they perceive them to be funny. Readers of technical groups, such as comp.lang.c� � value articles based
An Efficient Boosting Algorithm for Combining Preferences
, 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 ..."
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Cited by 707 (18 self)
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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 algorithm for combining preferences called RankBoost. We also describe an efficient implementation of the algorithm for certain natural cases. We discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different WWW 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 RankBoost to nearest-neighbor and regression algorithms.
Mining the Network Value of Customers
- In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining
, 2002
"... One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected pro t from a customer is greater than the cost of marketing to her, the marketing action for that customer is executed. So far, work in this area has considered only ..."
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Cited by 562 (11 self)
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One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected pro t from a customer is greater than the cost of marketing to her, the marketing action for that customer is executed. So far, work in this area has considered only the intrinsic value of the customer (i.e, the expected pro t from sales to her). We propose to model also the customer's network value: the expected pro t from sales to other customers she may inuence to buy, the customers those may inuence, and so on recursively. Instead of viewing a market as a set of independent entities, we view it as a social network and model it as a Markov random eld. We show the advantages of this approach using a social network mined from a collaborative ltering database. Marketing that exploits the network value of customers|also known as viral marketing|can be extremely eective, but is still a black art. Our work can be viewed as a step towards providing a more solid foundation for it, taking advantage of the availability of large relevant databases. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications| data mining
Cluster-Based Scalable Network Services
, 1997
"... This paper has benefited from the detailed and perceptive comments of our reviewers, especially our shepherd Hank Levy. We thank Randy Katz and Eric Anderson for their detailed readings of early drafts of this paper, and David Culler for his ideas on TACC's potential as a model for cluster prog ..."
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Cited by 402 (37 self)
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This paper has benefited from the detailed and perceptive comments of our reviewers, especially our shepherd Hank Levy. We thank Randy Katz and Eric Anderson for their detailed readings of early drafts of this paper, and David Culler for his ideas on TACC's potential as a model for cluster programming. Ken Lutz and Eric Fraser configured and administered the test network on which the TranSend scaling experiments were performed. Cliff Frost of the UC Berkeley Data Communications and Networks Services group allowed us to collect traces on the Berkeley dialup IP network and has worked with us to deploy and promote TranSend within Berkeley. Undergraduate researchers Anthony Polito, Benjamin Ling, and Andrew Huang implemented various parts of TranSend's user profile database and user interface. Ian Goldberg and David Wagner helped us debug TranSend, especially through their implementation of the rewebber
Re-place-ing Space: The Roles of Place and Space in Collaborative Systems
, 1996
"... Many collaborative and communicative environments use notions of “space ” and spatial organisation to facilitate and structure interaction. We argue that a focus on spatial models is misplaced. Drawing on understandings from architecture and urban design, as well as from our own research findings, w ..."
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Cited by 368 (4 self)
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Many collaborative and communicative environments use notions of “space ” and spatial organisation to facilitate and structure interaction. We argue that a focus on spatial models is misplaced. Drawing on understandings from architecture and urban design, as well as from our own research findings, we highlight the critical distinction between “space ” and “place”. While designers use spatial models to support interaction, we show how it is actually a notion of “place ” which frames interactive behaviour. This leads us to re-evaluate spatial systems, and discuss how “place”, rather than “space”, can support CSCW design.
