Bottyán Németh

Investigation of various matrix factorization methods for large recommender systems

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.

Matrix factorization and neighbor based algorithms for the netflix prize problem

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 (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 coefficients 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.

Improved Recognition of Spontaneous Hungarian Speech—Morphological and Acoustic Modeling Techniques for a Less Resourced Task

Various morphological and acoustic modeling techniques are evaluated on a less resourced, spontaneous Hungarian large-vocabulary continuous speech recognition (LVCSR) task. Among morphologically rich languages, Hungarian is known for its agglutinative, inflective nature that increases the data sparseness caused by a relatively small training database. Although Hungarian spelling is considered as simple phonological, a large part of the corpus is covered by words pronounced in multiple, phonemically different ways. Data-driven and language specific knowledge supported vocabulary decomposition methods are investigated in combination with phoneme- and grapheme-based acoustic modeling techniques on the given task. Word baseline and morph-based advanced baseline results are significantly outperformed by using both statistical and grammatical vocabulary decomposition methods. Although the discussed morph-based techniques recognize a significant amount of out of vocabulary words, the improvements are due not to this fact but to the reduction of insertion errors. Applying grapheme-based acoustic models instead of phoneme-based models causes no severe recognition performance deteriorations. Moreover, a fully data-driven acoustic modeling technique along with a statistical morphological modeling approach provides the best performance on the most difficult test set. The overall best speech recognition performance is obtained by using a novel word to morph decomposition technique that combines grammatical and unsupervised statistical segmentation algorithms. The improvement achieved by the proposed technique is stable across acoustic modeling approaches and larger with speaker adaptation.

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

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.

Towards automatic transcription of large spoken archives in agglutinating languages - Hungarian ASR for the MALACH project

The paper describes automatic speech recognition experiments and results on the spontaneous Hungarian MALACH speech corpus. A novel morph-based lexical modeling approach is compared to the traditional word-based one and to another, previously best performing morph-based one in terms of word and letter error rates. The applied language and acoustic modeling techniques are also detailed. Using unsupervised speaker adaptations along with morph based lexical models 14.4%-8.1% absolute word error rate reductions have been achieved on a 2 speakers, 2 hours test set as compared to the speaker independent baseline results.

Predicting user action from skin conductance

There are many studies focusing on enhancing physiological data in user interfaces. On one hand biofeedback games are using skin conductance and heart rate data to reflect the emotional state of the user, on the other hand BCI research tries to conclude user intentions from EEG signals. In our research we are collecting usual biofeedback data but process it with complex algorithms similarly to the BCI methodologies. This way we are able to conclude more complex user states than relaxation or anxiety. In our experiments we asked users to play with a simple arcade game, while we were recording physiological data. We were training artificial neural networks to learn the time of user action from the physiological signals. The networks were capable of detecting and also predicting user action 2 seconds before it was carried out.

Measurement-based fuzzy interpolation of room impulse responses

Application of room impulse responses (RIRs) to acoustic evaluation and auralization often requires many measurements to get enough information about the hall, or to provide enough flexibility for virtual sound source placements in convolution reverberation. In this paper we propose a measurement‐based fuzzy modeling method to approximate the RIR function at an arbitrary location between available measured points, without a priori information on the hall geometry or wall reflection parameters. For the fuzzy model identification we define an accuracy indicator of the spatial density of the source positions and predict the required number of them in a selected hall. This indicator quantifies the relationship of the early reflections, determined for various measured positions. This paper also proposes a method that treats nonuniform spatial sampling of the measurement positions, and its implementation for 2D cases is shown. Nonuniform spatial sampling can be useful when RIRs at some source positions ‐‐ e.g. ...

Visualization of movie features in collaborative filtering

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.

Your skin knows when you will jump

Motivation: Most of the affective gaming researches are focusing on using physiological measures to determine the emotional state of the user, while others try to create applications where the player can influence the game with his/her inner emotional states. We would like to mix these two approaches taking advantage of the psychological phenomena of anticipation of stress and using it up in games to predict user actions. Research Approach: Our research focuses on the user action in the course of play, and tries to establish a link between physiological parameters reflecting on the users emotional sate and the interaction he/she initiates in the game. Research Design: In our experiments we are recording skin conductance response while playing a simple arcade game. We train artificial neural networks to learn when the user interacts (jumps). Findings: In our paper we demonstrate that neural networks are not only capable of learning the exact time, but are also able to predict a jump 2 seconds before it is carried out only from the skin conductance data.