Results 1 - 10 of 191

Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System

by Badrul M. Sarwar, Joseph A. Konstan, Al Borchers, Jon Herlocker, Brad Miller, John Riedl , 1998
"... Collaborative filtering systems help address information overload by using the opinions of users in a community to make personal recommendations for documents to each user. Many collaborative filtering systems have few user opinions relative to the large number of documents available. This sparsity ..."
Abstract - Cited by 169 (16 self) - Add to MetaCart
Collaborative filtering systems help address information overload by using the opinions of users in a community to make personal recommendations for documents to each user. Many collaborative filtering systems have few user opinions relative to the large number of documents available. This sparsity

Experiences with GroupLens: Making Usenet Useful Again

by Bradley Miller, John T. Riedl, Joseph A. Konstan - Proceedings of the 1997 Usenix Winter Technical Conference , 1997
"... Collaborative filtering attempts to alleviate information overload by offering recommendations on whether information is valuable based on the opinions of those who have already evaluated it. Usenet news is an information source whose value is being severely diminished by the volume of low-quality a ..."
Abstract - Cited by 40 (5 self) - Add to MetaCart
and uninteresting information posted in its newsgroups. The GroupLens system applies collaborative filtering to Usenet news to demonstrate how we can restore the value of Usenet news by sharing our judgements of articles, with our identities protected by pseudonyms. This paper extends the original GroupLens work

Using Semi-intelligent Filtering Agents to Improve Prediction Quality in Collaborative Filtering Systems

by Badrul M. Sarwar , 1998
"... Collaborative filtering provides a solution to the problem of information overload by selecting items based on the subjective judgment of users who have already evaluated them. In high volume information sources the number of user evaluations available is only a small fraction of the total number of ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
filterbots as an alternative solution. We present a framework for incorporating filterbots in collaborative filtering and describe experimental results from incorporating filterbots in the GroupLens collaborative filtering system. We implemented three different filterbots that rate articles based

Experiences with GroupLens: Making Usenet Useful Again

by Bradley Miller John, John T. Riedl, Joseph A. Konstan - Proceedings of the 1997 Usenix Winter Technical Conference , 1997
"... Collaborative filtering attempts to alleviate information overload by offering recommendations on whether information is valuable based on the opinions of those who have already evaluated it. Usenet news is an information source whose value is being severely diminished by the volume of low-quality a ..."
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and uninteresting information posted in its newsgroups. The GroupLens system applies collaborative filtering to Usenet news to demonstrate how we can restore the value of Usenet news by sharing our judgements of articles, with our identities protected by pseudonyms. This paper extends the original GroupLens work

Clustering Items for Collaborative Filtering

by Mark Connor, Jon Herlocker , 2001
"... This short paper reports on work in progress related to applying data partitioning/clustering algorithms to ratings data in collaborative filtering. We use existing data partitioning and clustering algorithms to partition the set of items based on user rating data. Predictions are then computed ..."
Abstract - Cited by 10 (0 self) - Add to MetaCart
independently within each partition. Ideally, partitioning will improve the quality of collaborative filtering predictions and increase the scalability of collaborative filtering systems. We report preliminary results that suggest that partitioning algorithms can greatly increase scalability, but we have

Improving Prediction Quality in Collaborative Filtering based on Clustering

by Taek-hun Kim, Seok-in Park, Sung-bong Yang - IEEE/WIC/ACM Int. Conf , 2008
"... In this paper we present the recommender systems that use the k-means clustering method in order to solve the problems associated with neighbor selection. The first method is to solve the problem in which customers belong to different clusters due to the distance-based characteristics despite the fa ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
In this paper we present the recommender systems that use the k-means clustering method in order to solve the problems associated with neighbor selection. The first method is to solve the problem in which customers belong to different clusters due to the distance-based characteristics despite

Attack resistant collaborative filtering

by Bhaskar Mehta, Wolfgang Nejdl - SIGIR ’08 Proc. Thirty-First Annual Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval, ACM , 2008
"... The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations. The presence of noise can violate modeling assum ..."
Abstract - Cited by 17 (0 self) - Add to MetaCart
assumptions and may thus lead to instabilities in estimation and prediction. Even worse, malicious users can deliberately insert attack profiles in an attempt to bias the recommender system to their benefit. While previous research has attempted to study the robustness of various existing Collaborative

A Hybrid Approach to Improving Scalability in Collaborative Filtering

by Pritha Ghosh, Prosenjit Gupta
"... The process of filtering information or patterns using techniques involving collaboration among multiple agents or data sources is known as collaborative filtering [14]. Applications of collaborative filtering typically involve very large data sets. Techniques of Collaborative filtering have been ap ..."
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The process of filtering information or patterns using techniques involving collaboration among multiple agents or data sources is known as collaborative filtering [14]. Applications of collaborative filtering typically involve very large data sets. Techniques of Collaborative filtering have been

Tag-aware recommender systems by fusion of collaborative filtering algorithms

by Karen H. L. Tso-sutter, Ro Balby Marinho, Lars Schmidt-thieme - In Proceedings of the 2nd ACM Symposium on Applied Computing , 1995
"... Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content informa ..."
Abstract - Cited by 84 (3 self) - Add to MetaCart
Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content

Using Collaborative Filtering to Predict User Utterances in Dialogue

by Ryuichiro Higashinaka, Noriaki Kawamae, Kohji Dohsaka, Hideki Isozaki
"... Abstract. This paper proposes using collaborative filtering, a technique for using other users ’ information to model the behavior of a certain user, to predict users ’ evaluative expressions for entities in dialogue. Previous studies have found that inducing users ’ empathic utterances towards syst ..."
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systems can improve user satisfaction. Predicting what users may utter and communicating this information in advance would make it easy for users to show empathy, leading to possible improvement in the quality of dialogue. Experimental results show that our approach, which uses the similarity users
Results 1 - 10 of 191