The Experts below are selected from a list of 12261 Experts worldwide ranked by ideXlab platform
Augusto Pucci - One of the best experts on this subject based on the ideXlab platform.
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IJCAI - ItemRank: a random-walk based Scoring Algorithm for recommender engines
2007Co-Authors: Marco Gori, Augusto PucciAbstract:Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present "ItemRank", a random-walk based Scoring Algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users. We tested our Algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [Fouss et al., 2005; Sarwar et al., 2002]). We compared ItemRank with other state-of-the-art ranking techniques (in particular the Algorithms described in [Fouss et al., 2005]). Our experiments show that ItemRank performs better than the other Algorithms we compared to and, at the same time, it is less complex than other proposed Algorithms with respect to memory usage and computational cost too.
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ItemRank: A random-walk based Scoring Algorithm for recommender engines
IJCAI International Joint Conference on Artificial Intelligence, 2007Co-Authors: Marco Gori, Augusto PucciAbstract:Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present "ItemRank", a random-walk based Scoring Algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users. We tested our Algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [Fouss et al., 2005; Sarwar et al., 2002]). We compared ItemRank with other state-of-the-art ranking techniques (in particular the Algorithms described in [Fouss et al., 2005]). Our experiments show that ItemRank performs better than the other Algorithms we compared to and, at the same time, it is less complex than other proposed Algorithms with respect to memory usage and computational cost too.
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a random walk based Scoring Algorithm applied to recommender engines
Web Mining and Web Usage Analysis, 2006Co-Authors: Augusto Pucci, Marco Gori, Marco MagginiAbstract:Recommender systems are an emerging technology that helps consumers find interesting products and useful resources. A recommender system makes personalized product suggestions by extracting knowledge from the previous users' interactions. In this paper, we present "Item-Rank", a random-walk based Scoring Algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users. We tested our Algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies and that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender systems (e.g. [1,2]). We compared ItemRank with other state-of-the-art ranking techniques on this task. Our experiments show that ItemRank performs better than the other Algorithms we compared to and, at the same time, it is less complex with respect to memory usage and computational cost too. The presentation of the method is accompanied by an analysis of the MovieLens data set main properties.
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WEBKDD - A random-walk based Scoring Algorithm applied to recommender engines
Advances in Web Mining and Web Usage Analysis, 2006Co-Authors: Augusto Pucci, Marco Gori, Marco MagginiAbstract:Recommender systems are an emerging technology that helps consumers find interesting products and useful resources. A recommender system makes personalized product suggestions by extracting knowledge from the previous users' interactions. In this paper, we present "Item-Rank", a random-walk based Scoring Algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users. We tested our Algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies and that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender systems (e.g. [1,2]). We compared ItemRank with other state-of-the-art ranking techniques on this task. Our experiments show that ItemRank performs better than the other Algorithms we compared to and, at the same time, it is less complex with respect to memory usage and computational cost too. The presentation of the method is accompanied by an analysis of the MovieLens data set main properties.
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a random walk based Scoring Algorithm with application to recommender systems for large scale e commerce
Knowledge Discovery and Data Mining, 2006Co-Authors: Marco Gori, Augusto PucciAbstract:Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present ”ItemRank”, a random–walk based Scoring Algorithm, which can be used to rank products according to expected user preferences, in order to recommend top– rank items to potentially interested users. We tested our Algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies, and we compared ItemRank with other state-of-the-art ranking techniques (in particular the Algorithms described in [1, 2]). Our experiments show that ItemRank performs better than the other Algorithms we compared to and, at the same time, it is less complex than other proposed Algorithms with respect to memory usage and computational cost too. The presentation of the method is accompanied by an analysis that helps to discover some intriguing properties of the MovieLens data set, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [1, 3]).
Matt Huenerfauth - One of the best experts on this subject based on the ideXlab platform.
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evaluating a dynamic time warping based Scoring Algorithm for facial expressions in asl animations
Conference of the International Speech Communication Association, 2015Co-Authors: Hernisa Kacorri, Matt HuenerfauthAbstract:Advancing the automatic synthesis of linguistically accurate and natural-looking American Sign Language (ASL) animations from an easy-to-update script would increase information accessibility for many people who are deaf by facilitating more ASL content to websites and media. We are investigating the production of ASL grammatical facial expressions and head movements coordinated with the manual signs that are crucial for the interpretation of signed sentences. It would be useful for researchers to have an automatic Scoring Algorithm that could be used to rate the similarity of two animation sequences of ASL facial movements (or an animation sequence and a motioncapture recording of a human signer). We present a novel, sign-language specific similarity Scoring Algorithm, based on Dynamic Time Warping (DTW), for facial expression performances and the results of a user-study in which the predictions of this Algorithm were compared to the judgments of ASL signers. We found that our Algorithm had significant correlations with participants’ comprehension scores for the animations and the degree to which they reported noticing specific facial expressions.
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SLPAT@Interspeech - Evaluating a Dynamic Time Warping Based Scoring Algorithm for Facial Expressions in ASL Animations
Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies, 2015Co-Authors: Hernisa Kacorri, Matt HuenerfauthAbstract:Advancing the automatic synthesis of linguistically accurate and natural-looking American Sign Language (ASL) animations from an easy-to-update script would increase information accessibility for many people who are deaf by facilitating more ASL content to websites and media. We are investigating the production of ASL grammatical facial expressions and head movements coordinated with the manual signs that are crucial for the interpretation of signed sentences. It would be useful for researchers to have an automatic Scoring Algorithm that could be used to rate the similarity of two animation sequences of ASL facial movements (or an animation sequence and a motioncapture recording of a human signer). We present a novel, sign-language specific similarity Scoring Algorithm, based on Dynamic Time Warping (DTW), for facial expression performances and the results of a user-study in which the predictions of this Algorithm were compared to the judgments of ASL signers. We found that our Algorithm had significant correlations with participants’ comprehension scores for the animations and the degree to which they reported noticing specific facial expressions.
Marco Gori - One of the best experts on this subject based on the ideXlab platform.
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IJCAI - ItemRank: a random-walk based Scoring Algorithm for recommender engines
2007Co-Authors: Marco Gori, Augusto PucciAbstract:Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present "ItemRank", a random-walk based Scoring Algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users. We tested our Algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [Fouss et al., 2005; Sarwar et al., 2002]). We compared ItemRank with other state-of-the-art ranking techniques (in particular the Algorithms described in [Fouss et al., 2005]). Our experiments show that ItemRank performs better than the other Algorithms we compared to and, at the same time, it is less complex than other proposed Algorithms with respect to memory usage and computational cost too.
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ItemRank: A random-walk based Scoring Algorithm for recommender engines
IJCAI International Joint Conference on Artificial Intelligence, 2007Co-Authors: Marco Gori, Augusto PucciAbstract:Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present "ItemRank", a random-walk based Scoring Algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users. We tested our Algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [Fouss et al., 2005; Sarwar et al., 2002]). We compared ItemRank with other state-of-the-art ranking techniques (in particular the Algorithms described in [Fouss et al., 2005]). Our experiments show that ItemRank performs better than the other Algorithms we compared to and, at the same time, it is less complex than other proposed Algorithms with respect to memory usage and computational cost too.
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a random walk based Scoring Algorithm applied to recommender engines
Web Mining and Web Usage Analysis, 2006Co-Authors: Augusto Pucci, Marco Gori, Marco MagginiAbstract:Recommender systems are an emerging technology that helps consumers find interesting products and useful resources. A recommender system makes personalized product suggestions by extracting knowledge from the previous users' interactions. In this paper, we present "Item-Rank", a random-walk based Scoring Algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users. We tested our Algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies and that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender systems (e.g. [1,2]). We compared ItemRank with other state-of-the-art ranking techniques on this task. Our experiments show that ItemRank performs better than the other Algorithms we compared to and, at the same time, it is less complex with respect to memory usage and computational cost too. The presentation of the method is accompanied by an analysis of the MovieLens data set main properties.
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WEBKDD - A random-walk based Scoring Algorithm applied to recommender engines
Advances in Web Mining and Web Usage Analysis, 2006Co-Authors: Augusto Pucci, Marco Gori, Marco MagginiAbstract:Recommender systems are an emerging technology that helps consumers find interesting products and useful resources. A recommender system makes personalized product suggestions by extracting knowledge from the previous users' interactions. In this paper, we present "Item-Rank", a random-walk based Scoring Algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users. We tested our Algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies and that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender systems (e.g. [1,2]). We compared ItemRank with other state-of-the-art ranking techniques on this task. Our experiments show that ItemRank performs better than the other Algorithms we compared to and, at the same time, it is less complex with respect to memory usage and computational cost too. The presentation of the method is accompanied by an analysis of the MovieLens data set main properties.
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a random walk based Scoring Algorithm with application to recommender systems for large scale e commerce
Knowledge Discovery and Data Mining, 2006Co-Authors: Marco Gori, Augusto PucciAbstract:Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present ”ItemRank”, a random–walk based Scoring Algorithm, which can be used to rank products according to expected user preferences, in order to recommend top– rank items to potentially interested users. We tested our Algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies, and we compared ItemRank with other state-of-the-art ranking techniques (in particular the Algorithms described in [1, 2]). Our experiments show that ItemRank performs better than the other Algorithms we compared to and, at the same time, it is less complex than other proposed Algorithms with respect to memory usage and computational cost too. The presentation of the method is accompanied by an analysis that helps to discover some intriguing properties of the MovieLens data set, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [1, 3]).
Debasis Dash - One of the best experts on this subject based on the ideXlab platform.
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masswiz a novel Scoring Algorithm with target decoy based analysis pipeline for tandem mass spectrometry
Journal of Proteome Research, 2011Co-Authors: Amit Kumar Yadav, Dhirendra Kumar, Debasis DashAbstract:Mass spectrometry has made rapid advances in the recent past and has become the preferred method for proteomics. Although many open source Algorithms for peptide identification exist, such as X!Tandem and OMSSA, it has majorly been a domain of proprietary software. There is a need for better, freely available, and configurable Algorithms that can help in identifying the correct peptides while keeping the false positives to a minimum. We have developed MassWiz, a novel empirical Scoring function that gives appropriate weights to major ions, continuity of b-y ions, intensities, and the supporting neutral losses based on the instrument type. We tested MassWiz accuracy on 486,882 spectra from a standard mixture of 18 proteins generated on 6 different instruments downloaded from the Seattle Proteome Center public repository. We compared the MassWiz Algorithm with Mascot, Sequest, OMSSA, and X!Tandem at 1% FDR. MassWiz outperformed all in the largest data set (AGILENT XCT) and was second only to Mascot in the o...
Hernisa Kacorri - One of the best experts on this subject based on the ideXlab platform.
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evaluating a dynamic time warping based Scoring Algorithm for facial expressions in asl animations
Conference of the International Speech Communication Association, 2015Co-Authors: Hernisa Kacorri, Matt HuenerfauthAbstract:Advancing the automatic synthesis of linguistically accurate and natural-looking American Sign Language (ASL) animations from an easy-to-update script would increase information accessibility for many people who are deaf by facilitating more ASL content to websites and media. We are investigating the production of ASL grammatical facial expressions and head movements coordinated with the manual signs that are crucial for the interpretation of signed sentences. It would be useful for researchers to have an automatic Scoring Algorithm that could be used to rate the similarity of two animation sequences of ASL facial movements (or an animation sequence and a motioncapture recording of a human signer). We present a novel, sign-language specific similarity Scoring Algorithm, based on Dynamic Time Warping (DTW), for facial expression performances and the results of a user-study in which the predictions of this Algorithm were compared to the judgments of ASL signers. We found that our Algorithm had significant correlations with participants’ comprehension scores for the animations and the degree to which they reported noticing specific facial expressions.
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SLPAT@Interspeech - Evaluating a Dynamic Time Warping Based Scoring Algorithm for Facial Expressions in ASL Animations
Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies, 2015Co-Authors: Hernisa Kacorri, Matt HuenerfauthAbstract:Advancing the automatic synthesis of linguistically accurate and natural-looking American Sign Language (ASL) animations from an easy-to-update script would increase information accessibility for many people who are deaf by facilitating more ASL content to websites and media. We are investigating the production of ASL grammatical facial expressions and head movements coordinated with the manual signs that are crucial for the interpretation of signed sentences. It would be useful for researchers to have an automatic Scoring Algorithm that could be used to rate the similarity of two animation sequences of ASL facial movements (or an animation sequence and a motioncapture recording of a human signer). We present a novel, sign-language specific similarity Scoring Algorithm, based on Dynamic Time Warping (DTW), for facial expression performances and the results of a user-study in which the predictions of this Algorithm were compared to the judgments of ASL signers. We found that our Algorithm had significant correlations with participants’ comprehension scores for the animations and the degree to which they reported noticing specific facial expressions.
