István Pilászy

24723124100

Publications - 7

Visualization of movie features in collaborative filtering

Bottyán Németh István Pilászy Gábor Takács Domonkos Tikk

Publication Name: Somet 2013 12th IEEE International Conference on Intelligent Software Methodologies Tools and Techniques Proceedings

Publication Date: 2013-01-01

Volume: Unknown

Issue: Unknown

Page Range: 229-233

Description:

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. © 2013 IEEE.

Open Access: Yes

DOI: 10.1109/SoMeT.2013.6645674

Applications of the conjugate gradient method for implicit feedback collaborative filtering

István Pilászy Gábor Takács Domonkos Tikk

Publication Name: Recsys 11 Proceedings of the 5th ACM Conference on Recommender Systems

Publication Date: 2011-12-06

Volume: Unknown

Issue: Unknown

Page Range: 297-300

Description:

The need for solving weighted ridge regression (WRR) problems arises in a number of collaborative filtering (CF) algorithms. Often, there is not enough time to calculate the exact solution of the WRR problem, or it is not required. The conjugate gradient (CG) method is a state-of-the-art approach for the approximate solution of WRR problems. In this paper, we investigate some applications of the CG method for new and existing implicit feedback CF models. We demonstrate through experiments on the Netflix dataset that CG can be an efficient tool for training implicit feedback CF models. © 2011 ACM.

Open Access: Yes

DOI: 10.1145/2043932.2043987

Scalable collaborative filtering approaches for large reeommender systems

Bottyán Németh István Pilászy Gábor Takács Domonkos Tikk

Publication Name: Journal of Machine Learning Research

Publication Date: 2009-01-01

Volume: 10

Issue: Unknown

Page Range: 623-656

Description:

The collaborative filtering (CF) using known user ratings of items has proved to be effective for predicting user preferences in item selection. This thriving subfield of machine learning became popular in the late 1990s with the spread of online services that use recommender systems, such as Amazon, Yahoo! Music, and Netflix. CF approaches are usually designed to work on very large data sets. Therefore the scalability of the methods is crucial. In this work, we propose various scalable solutions that are validated against the Netflix Prize data set, currently the largest publicly available collection. First, we propose various matrix factorization (MF) based techniques. Second, a neighbor correction method for MF is outlined, which alloys the global perspective of MF and the localized property of neighbor based approaches efficiently. In the experimentation section, we first report on some implementation issues, and we suggest on how parameter optimization can be performed efficiently for MFs. We then show that the proposed scalable approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. Finally, we report on some experiments performed on MovieLens and Jester data sets.

Open Access: Yes

DOI: DOI not available

A unified approach of factor models and neighbor based methods for large recommender systems

Bottyán Németh István Pilászy Gábor Takács Domonkos Tikk

Publication Name: 1st International Conference on the Applications of Digital Information and Web Technologies Icadiwt 2008

Publication Date: 2008-12-30

Volume: Unknown

Issue: Unknown

Page Range: 186-191

Description:

Matrix factorization (MF) based approaches have proven to be efficient for rating-based recommendation systems. In this paper, we propose a hybrid approach that alloys an improved MF and the so-called NSVD1 approach, resulting in a very accurate factor model. After that, we propose a unification of factor models and neighbor based approaches, which further improves the performance. The approaches are evaluated on the Netflix Prize dataset, and they provide very low RMSE, and favorable running time. Our best solution presented here with Quiz RMSE 0.8851 outperforms all published single methods in the literature. ©2008 IEEE.

Open Access: Yes

DOI: 10.1109/ICADIWT.2008.4664342

Matrix factorization and neighbor based algorithms for the netflix prize problem

Bottyán Németh István Pilászy Gábor Takács Domonkos Tikk

Publication Name: Recsys 08 Proceedings of the 2008 ACM Conference on Recommender Systems

Publication Date: 2008-12-01

Volume: Unknown

Issue: Unknown

Page Range: 267-274

Description:

Collaborative filtering (CF) approaches proved to be effective for recommender systems in predicting user preferences in item selection using known user ratings of items. This subfield of machine learning has gained a lot of popularity with the Netix Prize competition started in October 2006. Two major approaches for this problem are matrix factorization (MF) and the neighbor based approach (NB). In this work, we propose various variants of MF and NB that can boost the performance of the usual ensemble based scheme. First, we investigate various regularization scenarios for MF. Second, we introduce two NB methods: one is based on correlation coeficients and the other on linear least squares. At the experimentation part, we show that the proposed approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. We present results of blending the proposed methods. © 2008 ACM.

Open Access: Yes

DOI: 10.1145/1454008.1454049

Investigation of various matrix factorization methods for large recommender systems

Bottyán Németh István Pilászy Gábor Takács Domonkos Tikk

Publication Name: Proceedings IEEE International Conference on Data Mining Workshops Icdm Workshops 2008

Publication Date: 2008-12-01

Volume: Unknown

Issue: Unknown

Page Range: 553-562

Description:

Matrix Factorization (MF) based approaches have proven to be efficient for rating-based recommendation systems. In this work, we propose several matrix factorization approaches with improved prediction accuracy. We introduce a novel and fast (semi)-positive MF approach that approximates the features by using positive values for either users or items. We describe a momentum-based MF approach. A transductive version of MF is also introduced, which uses information from test instances (namely the ratings users have given for certain items) to improve prediction accuracy. We describe an incremental variant of MF that efficiently handles new users/ratings, which is crucial in a real-life recommender system. A hybrid MF-neighborbased method is also discussed that further improves the performance of MF. The proposed methods are evaluated on the Netflix Prize dataset, and we show that they can achieve very favorable Quiz RMSE (best single method: 0.8904, combination: 0.8841) and running time. © 2008 IEEE.

Open Access: Yes

DOI: 10.1109/ICDMW.2008.86

Investigation of various matrix factorization methods for large recommender systems

Bottyán Németh István Pilászy Gábor Takács Domonkos Tikk

Publication Name: Proceedings of the 2nd Kdd Workshop on Large Scale Recommender Systems and the Netflix Prize Competition Netflix 08

Publication Date: 2008-12-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

Matrix Factorization (MF) based approaches have proven to be efficient for rating-based recommendation systems. In this work, we propose several matrix factorization approaches with improved prediction accuracy. We introduce a novel and fast (semi)-positive MF approach that approximates the features by using positive values for either users or items. We describe a momentum-based MF approach. A transductive version of MF is also introduced, which uses information from test instances (namely the ratings users have given for certain items) to improve prediction accuracy. We describe an incremental variant of MF that efficiently handles new users/ratings, which is crucial in a real-life recommender system. A hybrid MF - neighbor-based method is also discussed that further improves the performance of MF. The proposed methods are evaluated on the Netflix Prize dataset, and we show that they can achieve very favorable Quiz RMSE (best single method: 0.8904, combination: 0.8841) and running time. Copyright 2008 ACM.

Open Access: Yes

DOI: 10.1145/1722149.1722155