#### Filter Results:

#### Publication Year

2005

2013

#### Publication Type

#### Co-author

#### Publication Venue

#### Key Phrases

Learn More

- Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk
- Journal of Machine Learning Research
- 2009

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… (More)

- István Pilászy, Domonkos Tikk
- RecSys
- 2009

The Netflix Prize (NP) competition gave much attention to collaborative filtering (CF) approaches. Matrix factorization (MF) based CF approaches assign low dimensional feature vectors to users and items. We link CF and content-based filtering (CBF) by finding a linear transformation that transforms user or item descriptions so that they are as close as… (More)

- Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk
- RecSys
- 2008

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 Netflix Prize competition started in October 2006. Two major approaches for this problem are matrix factorization… (More)

- István Pilászy, Dávid Zibriczky, Domonkos Tikk
- RecSys
- 2010

Alternating least squares (ALS) is a powerful matrix factorization (MF) algorithm for both explicit and implicit feedback based recommender systems. As shown in many articles, increasing the number of latent factors (denoted by <i>K</i>) boosts the prediction accuracy of MF based recommender systems, including ALS as well. The price of the better accuracy… (More)

- G. Takacs, I. Pilaszy, B. Nemeth, D. Tikk
- 2008 First International Conference on the…
- 2008

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… (More)

- Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk
- 2008 IEEE International Conference on Data Mining…
- 2008

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… (More)

The Netflix Prize is a collaborative filtering problem. This subfield of machine learning has become popular from the late 1990s with the spread of online services that use recommendation systems, such as e.g. Amazon, Yahoo! Music , and of course Netflix. The aim of such a system is to predict what items a user might like based on his/her and other users… (More)

- Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk
- SIGKDD Explorations
- 2007

The Netflix Prize is a collaborative filtering problem. This subfield of machine learning became popular in the late 1990s with the spread of online services that used recommendation systems (e.g. Amazon, Yahoo! Music, and of course Netflix). The aim of such a system is to predict what items a user might like based on his/her and other users' previous… (More)

- István Pilászy
- 2005

Text categorization is used to automatically assign previously unseen documents to a predefined set of categories. This paper gives a short introduction into text categorization (TC), and describes the most important tasks of a text categorization system. It also focuses on Support Vector Machines (SVMs), the most popular machine learning algorithm used for… (More)

- Gábor Takács, István Pilászy, Domonkos Tikk
- RecSys
- 2011

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,… (More)