Domonkos Tikk | Budapest University of Technology and Economics (original) (raw)
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Papers by Domonkos Tikk
A system and method of providing personalized item recommendations in a communication system comp... more A system and method of providing personalized item recommendations in a communication system comprising a server and a plurality of client devices. At the server, a plurality of user rating vectors are received from a plurality of client devices and aggregated into a rating matrix that is factorized into a user feature matrix and an item feature matrix, with the product of the user feature and item feature matrixes approximating the user rating matrix. The factorization comprises the steps of the ALS1 or the IALS1 algorithm including: initializing the user feature matrix and the item feature matrix with predefined initial values; alternately optimizing the user feature matrix and the item feature matrix until a termination condition is met. The item feature matrix is transmitted from the server to at least one client device, and a predictive rating vector is generated as the product of the associated user feature vector and the item feature matrix. At least one item is selected for ...
2013 IEEE 12th International Conference on Intelligent Software Methodologies, Tools and Techniques (SoMeT), 2013
ABSTRACT In this paper we will describe a modification of the matrix factorization (MF) algorithm... more ABSTRACT In this paper we will describe a modification of the matrix factorization (MF) algorithm which allows visualizing the user and item characteristics. When applying MF for collaborative filtering, we get a model that represents the attributes of users and items by feature vectors. Some elements of these vectors may have understandable meaning for humans but due to the lack of internal connections between the feature vectors, these are difficult to visualize. In this paper we give a detailed description of a MF method enabling better visualization of features by arranging them into a 2D map, where via the calculation of the feature values we try to position features with similar “meaning” close to each other. To achieve this first we define a neighborhood relation on features, then we modify the MF so that we introduce a new term in the error function which penalize the difference between the neighbor features. We show that this modification slightly decrease the accuracy of the model but we get well visualized feature maps. On the feature maps meanings can be associated with regions, and so we can provide an interesting explanation for the user why he/she was recommended the movie. Such plausible explanations may result in that users will better understand how the system works, which can also increase customer loyalty towards the service provider.
Proceedings of the sixth ACM conference on Recommender systems - RecSys '12, 2012
International Journal of Fuzzy Systems
Journal of Information Processing Systems
ACM SIGKDD Explorations Newsletter, 2006
We describe our approach for the extraction of drug-drug in-teractions from literature. The propo... more We describe our approach for the extraction of drug-drug in-teractions from literature. The proposed method builds majority voting ensembles of contrasting machine learning methods, which exploit differ-ent linguistic feature spaces. We evaluated our approach in the context of the DDI Extraction 2011 challenge, where using document-wise cross-validation, the best single classifier achieved an F1 of 57.3 % and the best ensemble achieved 60.6 %. On the held out test set, our best run achieved a F1 of 65.7 %.
Studies in Fuzziness and Soft Computing, 2003
A system and method of providing personalized item recommendations in a communication system comp... more A system and method of providing personalized item recommendations in a communication system comprising a server and a plurality of client devices. At the server, a plurality of user rating vectors are received from a plurality of client devices and aggregated into a rating matrix that is factorized into a user feature matrix and an item feature matrix, with the product of the user feature and item feature matrixes approximating the user rating matrix. The factorization comprises the steps of the ALS1 or the IALS1 algorithm including: initializing the user feature matrix and the item feature matrix with predefined initial values; alternately optimizing the user feature matrix and the item feature matrix until a termination condition is met. The item feature matrix is transmitted from the server to at least one client device, and a predictive rating vector is generated as the product of the associated user feature vector and the item feature matrix. At least one item is selected for ...
2013 IEEE 12th International Conference on Intelligent Software Methodologies, Tools and Techniques (SoMeT), 2013
ABSTRACT In this paper we will describe a modification of the matrix factorization (MF) algorithm... more ABSTRACT In this paper we will describe a modification of the matrix factorization (MF) algorithm which allows visualizing the user and item characteristics. When applying MF for collaborative filtering, we get a model that represents the attributes of users and items by feature vectors. Some elements of these vectors may have understandable meaning for humans but due to the lack of internal connections between the feature vectors, these are difficult to visualize. In this paper we give a detailed description of a MF method enabling better visualization of features by arranging them into a 2D map, where via the calculation of the feature values we try to position features with similar “meaning” close to each other. To achieve this first we define a neighborhood relation on features, then we modify the MF so that we introduce a new term in the error function which penalize the difference between the neighbor features. We show that this modification slightly decrease the accuracy of the model but we get well visualized feature maps. On the feature maps meanings can be associated with regions, and so we can provide an interesting explanation for the user why he/she was recommended the movie. Such plausible explanations may result in that users will better understand how the system works, which can also increase customer loyalty towards the service provider.
Proceedings of the sixth ACM conference on Recommender systems - RecSys '12, 2012
International Journal of Fuzzy Systems
Journal of Information Processing Systems
ACM SIGKDD Explorations Newsletter, 2006
We describe our approach for the extraction of drug-drug in-teractions from literature. The propo... more We describe our approach for the extraction of drug-drug in-teractions from literature. The proposed method builds majority voting ensembles of contrasting machine learning methods, which exploit differ-ent linguistic feature spaces. We evaluated our approach in the context of the DDI Extraction 2011 challenge, where using document-wise cross-validation, the best single classifier achieved an F1 of 57.3 % and the best ensemble achieved 60.6 %. On the held out test set, our best run achieved a F1 of 65.7 %.
Studies in Fuzziness and Soft Computing, 2003