The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
Martin Ester - One of the best experts on this subject based on the ideXlab platform.
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a matrix Factorization Technique with trust propagation for recommendation in social networks
Conference on Recommender Systems, 2010Co-Authors: Mohsen Jamali, Martin EsterAbstract:Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network among users and makes recommendations for a user based on the ratings of the users that have direct or indirect social relations with the given user. As one of their major benefits, social network based approaches have been shown to reduce the problems with cold start users. In this paper, we explore a model-based approach for recommendation in social networks, employing matrix Factorization Techniques. Advancing previous work, we incorporate the mechanism of trust propagation into the model. Trust propagation has been shown to be a crucial phenomenon in the social sciences, in social network analysis and in trust-based recommendation. We have conducted experiments on two real life data sets, the public domain Epinions.com dataset and a much larger dataset that we have recently crawled from Flixster.com. Our experiments demonstrate that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.
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RecSys - A matrix Factorization Technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems - RecSys '10, 2010Co-Authors: Mohsen Jamali, Martin EsterAbstract:Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network among users and makes recommendations for a user based on the ratings of the users that have direct or indirect social relations with the given user. As one of their major benefits, social network based approaches have been shown to reduce the problems with cold start users. In this paper, we explore a model-based approach for recommendation in social networks, employing matrix Factorization Techniques. Advancing previous work, we incorporate the mechanism of trust propagation into the model. Trust propagation has been shown to be a crucial phenomenon in the social sciences, in social network analysis and in trust-based recommendation. We have conducted experiments on two real life data sets, the public domain Epinions.com dataset and a much larger dataset that we have recently crawled from Flixster.com. Our experiments demonstrate that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.
Mohsen Jamali - One of the best experts on this subject based on the ideXlab platform.
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a matrix Factorization Technique with trust propagation for recommendation in social networks
Conference on Recommender Systems, 2010Co-Authors: Mohsen Jamali, Martin EsterAbstract:Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network among users and makes recommendations for a user based on the ratings of the users that have direct or indirect social relations with the given user. As one of their major benefits, social network based approaches have been shown to reduce the problems with cold start users. In this paper, we explore a model-based approach for recommendation in social networks, employing matrix Factorization Techniques. Advancing previous work, we incorporate the mechanism of trust propagation into the model. Trust propagation has been shown to be a crucial phenomenon in the social sciences, in social network analysis and in trust-based recommendation. We have conducted experiments on two real life data sets, the public domain Epinions.com dataset and a much larger dataset that we have recently crawled from Flixster.com. Our experiments demonstrate that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.
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RecSys - A matrix Factorization Technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems - RecSys '10, 2010Co-Authors: Mohsen Jamali, Martin EsterAbstract:Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network among users and makes recommendations for a user based on the ratings of the users that have direct or indirect social relations with the given user. As one of their major benefits, social network based approaches have been shown to reduce the problems with cold start users. In this paper, we explore a model-based approach for recommendation in social networks, employing matrix Factorization Techniques. Advancing previous work, we incorporate the mechanism of trust propagation into the model. Trust propagation has been shown to be a crucial phenomenon in the social sciences, in social network analysis and in trust-based recommendation. We have conducted experiments on two real life data sets, the public domain Epinions.com dataset and a much larger dataset that we have recently crawled from Flixster.com. Our experiments demonstrate that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.
Flavius Frasincar - One of the best experts on this subject based on the ideXlab platform.
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an elastic net regularized matrix Factorization Technique for recommender systems
ACM Symposium on Applied Computing, 2020Co-Authors: Bianca Mitroi, Flavius FrasincarAbstract:Matrix Factorization models are becoming increasingly popular in the field of collaborative filtering recommender systems. Recent developments in this area of research use a penalization method, such as the L2 penalty, to restrict overfitting and reduce sparseness. We propose an alternative way of regularizing matrix Factorization for recommender systems, i.e., the elastic net. A compromise between the L1 and L2 penalties, the elastic net can be implemented in any coefficient estimation scenario. We evaluate the performance of our model on real-world data, namely the MovieLens 100K dataset. Comparison with two more restrictive models shows that our proposed regularization provides superior accuracy in predictions, as measured by the mean absolute error. Moreover, prediction errors for individual users occur less often, and we are able to accurately predict 95.02% of the ratings with an error of at most two points from the real ratings, given on a scale from 1 to 5. Finally, sensitivity analysis shows the stability of the proposed solution.
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SAC - An elastic net regularized matrix Factorization Technique for recommender systems
Proceedings of the 35th Annual ACM Symposium on Applied Computing, 2020Co-Authors: Bianca Mitroi, Flavius FrasincarAbstract:Matrix Factorization models are becoming increasingly popular in the field of collaborative filtering recommender systems. Recent developments in this area of research use a penalization method, such as the L2 penalty, to restrict overfitting and reduce sparseness. We propose an alternative way of regularizing matrix Factorization for recommender systems, i.e., the elastic net. A compromise between the L1 and L2 penalties, the elastic net can be implemented in any coefficient estimation scenario. We evaluate the performance of our model on real-world data, namely the MovieLens 100K dataset. Comparison with two more restrictive models shows that our proposed regularization provides superior accuracy in predictions, as measured by the mean absolute error. Moreover, prediction errors for individual users occur less often, and we are able to accurately predict 95.02% of the ratings with an error of at most two points from the real ratings, given on a scale from 1 to 5. Finally, sensitivity analysis shows the stability of the proposed solution.
Fateme Olia - One of the best experts on this subject based on the ideXlab platform.
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Solving linear systems over idempotent semifields through LU-Factorization
Rendiconti del Circolo Matematico di Palermo Series 2, 2020Co-Authors: Sedighe Jamshidvand, Shaban Ghalandarzadeh, Amirhossein Amiraslani, Fateme OliaAbstract:In this paper, we introduce and analyze a new LU -Factorization Technique for square matrices over idempotent semifields. In particular, more emphasis is put on “max-plus” algebra here but the work is extended to other idempotent semifields as well. We first determine the conditions under which a square matrix has LU factors. Next, using this Technique, we propose a method for solving square linear systems of equations whose system matrices are LU -factorizable. We also give conditions for an LU -factorizable system to have solutions. This work is an extension of similar Techniques over fields. Maple^® procedures for this LU -Factorization are also included.
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Solving Linear Systems over Idempotent Semifields through $LU$-Factorization
arXiv: Commutative Algebra, 2019Co-Authors: Sedighe Jamshidvand, Shaban Ghalandarzadeh, Amirhossein Amiraslani, Fateme OliaAbstract:In this paper, we introduce and analyze a new $LU$-Factorization Technique for square matrices over idempotent semifields. In particular, more emphasis is put on "max-plus" algebra here, but the work is extended to other idempotent semifields as well. We first determine the conditions under which a square matrix has $LU$ factors. Next, using this Technique, we propose a method for solving square linear systems of equations whose system matrices are $LU$-factorizable. We also give conditions for an $LU$-factorizable system to have solutions. This work is an extension of similar Techniques over fields. Maple procedures for this $LU$-Factorization are also included.
Subir Bandyopadhyay - One of the best experts on this subject based on the ideXlab platform.
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a multilevel Factorization Technique for pass transistor logic
International Conference on VLSI Design, 1996Co-Authors: Arunita Jaekel, G A Jullien, Subir BandyopadhyayAbstract:Pass transistor logic (PTL) networks have been used by many researchers to design fast, area efficient pipelined systems. Not much work has been done in the area of multilevel logic synthesis in PTL networks. In this paper, we have investigated the use of algebraic Factorization Techniques to synthesize multilevel PTL networks.
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VLSI Design - A multilevel Factorization Technique for pass transistor logic
Proceedings of 9th International Conference on VLSI Design, 1Co-Authors: Arunita Jaekel, G A Jullien, Subir BandyopadhyayAbstract:Pass transistor logic (PTL) networks have been used by many researchers to design fast, area efficient pipelined systems. Not much work has been done in the area of multilevel logic synthesis in PTL networks. In this paper, we have investigated the use of algebraic Factorization Techniques to synthesize multilevel PTL networks.
