The Experts below are selected from a list of 123 Experts worldwide ranked by ideXlab platform
Marco Viviani - One of the best experts on this subject based on the ideXlab platform.
-
Multi-application Personalization: Data Propagation Evaluation on a Real-life Search Query Log
2012Co-Authors: Marco Viviani, Nadia Bennani, Elod Egyed-zsigmond, Lyes Limam, David CoquilAbstract:In the field of multi-application personalization, several techniques have been proposed to support User modeling. None of them have sufficiently investigated the opportunity for a multi-application Profile to evolve over time in order to avoid Data inconsistency and the subsequent loss of income for website Users and companies. In this paper, we propose a model addressing this issue and we focus in particular on User Profile Data propagation management, as a way to reduce the amount of inconsistent User Profile information over several applications. To evaluate our model, we first extract User Profiles using logs of the large real-life AOL search engine. Then, we simulate Data propagation along semantically related User information.
-
multi application Profile updates propagation a semantic layer to improve mapping between applications
The Web Conference, 2012Co-Authors: Nadia Bennani, Max Chevalier, Elod Egyedzsigmond, Gilles Hubert, Marco Viviani, Nadia Bennani, Marco VivianiAbstract:In the field of multi-application personalization, several techniques have been proposed to support User modeling for User Data management across different applications. Many of them are based on Data reconciliation techniques often implying the concepts of static ontologies and generic User Data models. None of them have sufficiently investigated two main issues related to User modeling: (1) Profile definition in order to allow every application to build their own view of Users while promoting the sharing of these Profiles and (2) Profile evolution over time in order to avoid Data inconsistency and the subsequent loss of income for web-site Users and companies. In this paper, we conduct work and propose separated solutions for every issue. We propose a flexible User modeling system, not imposing any fixed User model whom different applications should conform to, but based on the concept of mapping among applications (and mapping functions among their User attributes). We focus in particular on the management of User Profile Data propagation, as a way to reduce the amount of inconsistent User Profile information over several applications. A second goal of this paper is to illustrate, in this context, the benefit obtained by the integration of a Semantic Layer that can help application designers to automatically identify potential User attribute mappings between applications. This paper so illustrates a work-in-progress work where two complementary approaches are integrated to improve a main goal: managing multi-application User Profiles in a semi-automatic manner.
-
WWW (Companion Volume) - Multi-application Profile updates propagation: a semantic layer to improve mapping between applications
Proceedings of the 21st international conference companion on World Wide Web - WWW '12 Companion, 2012Co-Authors: Nadia Bennani, Max Chevalier, Gilles Hubert, Marco Viviani, Elöd Egyed-zsigmond, Nadia Bennani, Elod Egyed-zsigmond, Marco VivianiAbstract:In the field of multi-application personalization, several techniques have been proposed to support User modeling for User Data management across different applications. Many of them are based on Data reconciliation techniques often implying the concepts of static ontologies and generic User Data models. None of them have sufficiently investigated two main issues related to User modeling: (1) Profile definition in order to allow every application to build their own view of Users while promoting the sharing of these Profiles and (2) Profile evolution over time in order to avoid Data inconsistency and the subsequent loss of income for web-site Users and companies. In this paper, we conduct work and propose separated solutions for every issue. We propose a flexible User modeling system, not imposing any fixed User model whom different applications should conform to, but based on the concept of mapping among applications (and mapping functions among their User attributes). We focus in particular on the management of User Profile Data propagation, as a way to reduce the amount of inconsistent User Profile information over several applications. A second goal of this paper is to illustrate, in this context, the benefit obtained by the integration of a Semantic Layer that can help application designers to automatically identify potential User attribute mappings between applications. This paper so illustrates a work-in-progress work where two complementary approaches are integrated to improve a main goal: managing multi-application User Profiles in a semi-automatic manner.
-
Multi-application Personalization using G-Profile
IADIS International Journal on Computer Science and Information System, 2011Co-Authors: Marco Viviani, Nadia Bennani, Elod Egyed-zsigmondAbstract:Sharing heterogeneous Data among distributed environments in a User- centric way represents today the main challenge for personalization. In recent years several techniques have been proposed to support User mod- eling for multi-application personalization. In this paper we describe G- Profile, our multi-application User modeling system. G-Profile represents a way to address some open issues not sufficiently investigated in litera- ture, as the opportunity for a multi-application Profile to evolve over the time, together with the possibility to guarantee security and privacy in the diffusion of User information among applications. In particular, this paper focuses on User Profile Data modifications propagation, aiming to reduce the amount of incoherent information about the User over several applications.
Lee C Giles - One of the best experts on this subject based on the ideXlab platform.
-
collaborative filtering by personality diagnosis a hybrid memory and model based approach
arXiv: Information Retrieval, 2013Co-Authors: David M Pennock, Eric Horvitz, Steve Lawrence, Lee C GilesAbstract:The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple Users to recommend items of interest to other Users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed and evaluated many approaches for generating recommendations. We describe and evaluate a new method called emph{personality diagnosis (PD)}. Given a User's preferences for some items, we compute the probability that he or she is of the same "personality type" as other Users, and, in turn, the probability that he or she will like new items. PD retains some of the advantages of traditional similarity-weighting techniques in that all Data is brought to bear on each prediction and new Data can be added easily and incrementally. Additionally, PD has a meaningful probabilistic interpretation, which may be leveraged to justify, explain, and augment results. We report empirical results on the EachMovie Database of movie ratings, and on User Profile Data collected from the CiteSeer digital library of Computer Science research papers. The probabilistic framework naturally supports a variety of descriptive measurements - in particular, we consider the applicability of a value of information (VOI) computation.
-
collaborative filtering by personality diagnosis a hybrid memory and model based approach
Uncertainty in Artificial Intelligence, 2000Co-Authors: David M Pennock, Eric Horvitz, Steve Lawrence, Lee C GilesAbstract:The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple Users to recommend items of interest to other Users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed and evaluated many approaches for generating recommendations. We describe and evaluate a new method called personality diagnosis (PD). Given a User's preferences for some items, we compute the probability that he or she is of the same "personality type" as other Users, and, in turn, the probability that he or she will like new items. PD retains some of the advantages of traditional similarity-weighting techniques in that all Data is brought to bear on each prediction and new Data can be added easily and incrementally. Additionally, PD has a meaningful probabilistic interpretation, which may be leveraged to justify, explain, and augment results. We report empirical results on the EachMovie Database of movie ratings, and on User Profile Data collected from the CiteSeer digital library of Computer Science research papers. The probabilistic framework naturally supports a variety of descriptive measurements--in particular, we consider the applicability of a value of information (VOI) computation.
Lina Yao - One of the best experts on this subject based on the ideXlab platform.
-
DeepStore: an interaction-aware Wide&Deep model for store site recommendation with attentional spatial embeddings
IEEE internet of things journal, 2019Co-Authors: Yan Liu, Bin Guo, Nuo Li, Jing Zhang, Jingmin Chen, Daqing Zhang, Yinxiao Liu, Zhiwen Yu, Sizhe Zhang, Lina YaoAbstract:Store site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource Data in cities has fostered unprecedented opportunities to the Data-driven store site recommendation, which aims at leveraging large-scale User-generated Data to analyze and mine Users' preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single Data source which lacks some significant Data (e.g., consumption Data and User Profile Data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different Users at the store based on multisource Data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low-and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource Data, we propose two embedding methods for different types of Data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world Dataset including store Data, User Data, and point-of-interest Data, the results demonstrate that DeepStore outperforms the state-of-the-art models.
-
deepstore an interaction aware wide deep model for store site recommendation with attentional spatial embeddings
IEEE Internet of Things Journal, 2019Co-Authors: Yan Liu, Bin Guo, Jing Zhang, Jingmin Chen, Daqing Zhang, Yinxiao Liu, Sizhe Zhang, Lina YaoAbstract:Store site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource Data in cities has fostered unprecedented opportunities to the Data-driven store site recommendation, which aims at leveraging large-scale User-generated Data to analyze and mine Users’ preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single Data source which lacks some significant Data (e.g., consumption Data and User Profile Data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different Users at the store based on multisource Data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low- and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource Data, we propose two embedding methods for different types of Data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world Dataset including store Data, User Data, and point-of-interest Data, the results demonstrate that DeepStore outperforms the state-of-the-art models.
-
DeepStore: an interaction-aware Wide&Deep model for store site recommendation with attentional spatial embeddings
IEEE internet of things journal, 2019Co-Authors: Yan Liu, Bin Guo, Jing Zhang, Jingmin Chen, Daqing Zhang, Yinxiao Liu, Sizhe Zhang, Lina YaoAbstract:Store site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource Data in cities has fostered unprecedented opportunities to the Data-driven store site recommendation, which aims at leveraging large-scale User-generated Data to analyze and mine Users' preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single Data source which lacks some significant Data (e.g., consumption Data and User Profile Data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different Users at the store based on multisource Data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low-and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource Data, we propose two embedding methods for different types of Data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world Dataset including store Data, User Data, and point-of-interest Data, the results demonstrate that DeepStore outperforms the state-of-the-art models.
Nadia Bennani - One of the best experts on this subject based on the ideXlab platform.
-
Multi-application Personalization: Data Propagation Evaluation on a Real-life Search Query Log
2012Co-Authors: Marco Viviani, Nadia Bennani, Elod Egyed-zsigmond, Lyes Limam, David CoquilAbstract:In the field of multi-application personalization, several techniques have been proposed to support User modeling. None of them have sufficiently investigated the opportunity for a multi-application Profile to evolve over time in order to avoid Data inconsistency and the subsequent loss of income for website Users and companies. In this paper, we propose a model addressing this issue and we focus in particular on User Profile Data propagation management, as a way to reduce the amount of inconsistent User Profile information over several applications. To evaluate our model, we first extract User Profiles using logs of the large real-life AOL search engine. Then, we simulate Data propagation along semantically related User information.
-
multi application Profile updates propagation a semantic layer to improve mapping between applications
The Web Conference, 2012Co-Authors: Nadia Bennani, Max Chevalier, Elod Egyedzsigmond, Gilles Hubert, Marco Viviani, Nadia Bennani, Marco VivianiAbstract:In the field of multi-application personalization, several techniques have been proposed to support User modeling for User Data management across different applications. Many of them are based on Data reconciliation techniques often implying the concepts of static ontologies and generic User Data models. None of them have sufficiently investigated two main issues related to User modeling: (1) Profile definition in order to allow every application to build their own view of Users while promoting the sharing of these Profiles and (2) Profile evolution over time in order to avoid Data inconsistency and the subsequent loss of income for web-site Users and companies. In this paper, we conduct work and propose separated solutions for every issue. We propose a flexible User modeling system, not imposing any fixed User model whom different applications should conform to, but based on the concept of mapping among applications (and mapping functions among their User attributes). We focus in particular on the management of User Profile Data propagation, as a way to reduce the amount of inconsistent User Profile information over several applications. A second goal of this paper is to illustrate, in this context, the benefit obtained by the integration of a Semantic Layer that can help application designers to automatically identify potential User attribute mappings between applications. This paper so illustrates a work-in-progress work where two complementary approaches are integrated to improve a main goal: managing multi-application User Profiles in a semi-automatic manner.
-
WWW (Companion Volume) - Multi-application Profile updates propagation: a semantic layer to improve mapping between applications
Proceedings of the 21st international conference companion on World Wide Web - WWW '12 Companion, 2012Co-Authors: Nadia Bennani, Max Chevalier, Gilles Hubert, Marco Viviani, Elöd Egyed-zsigmond, Nadia Bennani, Elod Egyed-zsigmond, Marco VivianiAbstract:In the field of multi-application personalization, several techniques have been proposed to support User modeling for User Data management across different applications. Many of them are based on Data reconciliation techniques often implying the concepts of static ontologies and generic User Data models. None of them have sufficiently investigated two main issues related to User modeling: (1) Profile definition in order to allow every application to build their own view of Users while promoting the sharing of these Profiles and (2) Profile evolution over time in order to avoid Data inconsistency and the subsequent loss of income for web-site Users and companies. In this paper, we conduct work and propose separated solutions for every issue. We propose a flexible User modeling system, not imposing any fixed User model whom different applications should conform to, but based on the concept of mapping among applications (and mapping functions among their User attributes). We focus in particular on the management of User Profile Data propagation, as a way to reduce the amount of inconsistent User Profile information over several applications. A second goal of this paper is to illustrate, in this context, the benefit obtained by the integration of a Semantic Layer that can help application designers to automatically identify potential User attribute mappings between applications. This paper so illustrates a work-in-progress work where two complementary approaches are integrated to improve a main goal: managing multi-application User Profiles in a semi-automatic manner.
-
Multi-application Personalization using G-Profile
IADIS International Journal on Computer Science and Information System, 2011Co-Authors: Marco Viviani, Nadia Bennani, Elod Egyed-zsigmondAbstract:Sharing heterogeneous Data among distributed environments in a User- centric way represents today the main challenge for personalization. In recent years several techniques have been proposed to support User mod- eling for multi-application personalization. In this paper we describe G- Profile, our multi-application User modeling system. G-Profile represents a way to address some open issues not sufficiently investigated in litera- ture, as the opportunity for a multi-application Profile to evolve over the time, together with the possibility to guarantee security and privacy in the diffusion of User information among applications. In particular, this paper focuses on User Profile Data modifications propagation, aiming to reduce the amount of incoherent information about the User over several applications.
David M Pennock - One of the best experts on this subject based on the ideXlab platform.
-
collaborative filtering by personality diagnosis a hybrid memory and model based approach
arXiv: Information Retrieval, 2013Co-Authors: David M Pennock, Eric Horvitz, Steve Lawrence, Lee C GilesAbstract:The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple Users to recommend items of interest to other Users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed and evaluated many approaches for generating recommendations. We describe and evaluate a new method called emph{personality diagnosis (PD)}. Given a User's preferences for some items, we compute the probability that he or she is of the same "personality type" as other Users, and, in turn, the probability that he or she will like new items. PD retains some of the advantages of traditional similarity-weighting techniques in that all Data is brought to bear on each prediction and new Data can be added easily and incrementally. Additionally, PD has a meaningful probabilistic interpretation, which may be leveraged to justify, explain, and augment results. We report empirical results on the EachMovie Database of movie ratings, and on User Profile Data collected from the CiteSeer digital library of Computer Science research papers. The probabilistic framework naturally supports a variety of descriptive measurements - in particular, we consider the applicability of a value of information (VOI) computation.
-
collaborative filtering by personality diagnosis a hybrid memory and model based approach
Uncertainty in Artificial Intelligence, 2000Co-Authors: David M Pennock, Eric Horvitz, Steve Lawrence, Lee C GilesAbstract:The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple Users to recommend items of interest to other Users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed and evaluated many approaches for generating recommendations. We describe and evaluate a new method called personality diagnosis (PD). Given a User's preferences for some items, we compute the probability that he or she is of the same "personality type" as other Users, and, in turn, the probability that he or she will like new items. PD retains some of the advantages of traditional similarity-weighting techniques in that all Data is brought to bear on each prediction and new Data can be added easily and incrementally. Additionally, PD has a meaningful probabilistic interpretation, which may be leveraged to justify, explain, and augment results. We report empirical results on the EachMovie Database of movie ratings, and on User Profile Data collected from the CiteSeer digital library of Computer Science research papers. The probabilistic framework naturally supports a variety of descriptive measurements--in particular, we consider the applicability of a value of information (VOI) computation.
