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Improved questionnaire trees for active learning in recommender systems 16

Improved questionnaire trees for active learning in recommen...

引用

16th Workshops on learning, Knowledge, Adaptation, LWA 2014: Knowledge Discovery, Data Mining and machine learning, KDML 2014, information Retrieval, IR 2014 and Knowledge Management, FGWM 2014

作者： Karimi, Rasoul Nanopoulos, Alexandros Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Marienburger Platz 22 Hildesheim31141 Germany Department of Business Informatics University of Eichstatt-Ingolstadt Schanz 49 Ingolstadt85049 Germany

A key challenge in recommender systems is how to profile new-users. This problem is called cold-start problem or new-user prob-lem. A well-known solution for this problem is to use active learning techniques and ask new users to rate a few items in order to reveal their preferences. Recently, questionnaire trees (tree structures) have been proposed to build such adaptive questionnaires. In this paper, we improve the questionnaire trees by splitting the nodes of the trees in afiner-grained fashion. Specifically, the nodes are split in a 6-way manner instead of 3-way split. Furthermore, we compare our approach to on- line updating and show that our method outperforms online updating in order to fold-in the new user into recommendation model. Finally, we develop three simple baselines based on the questionnaire trees and compare them against the state-of-the-art baseline to show that the new- user problem in recommender systems is tough and demands a mature solution. Copyright © 2014 by the paper's authors.

关键词： Recommender systems

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Realistic optimal policies for energy-efficient train driving

Realistic optimal policies for energy-efficient train drivin...

引用

International Conference on Intelligent Transportation

作者： Josif Grabocka Alexandros Dalkalitsis Athanasios Lois Evangelos Katsaros Lars Schmidt-Thieme Information Systems and Machine Learning Lab University of Hildesheim Germany TrainOSE S.A. Greece

Transportation is a crucial cog within the cog-wheel of our economies and modern lifestyles. Unfortunately, both the rising cost of energy production and the increasing demand for transportation pose the challenge of minimizing the energy consumption of automobiles. This paper proposes an offline driver behavior adaptation approach (eco-driving) for trains. An optimal driving behavior policy is computed using Simulated Annealing optimization search over a collection of real driving behavior data (realistic policy). Empirical findings show that if drivers would follow the recommended optimal policy, then an energy saving of up to 50 % is a realistic upper bound potential.

关键词： Optimized production technology Energy consumption Simulated annealing Vehicles Convergence

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An SVM Plait for Improving Affect Recognition in Intelligent Tutoring systems

An SVM Plait for Improving Affect Recognition in Intelligent...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Ruth Janning Carlotta Schatten Lars Schmidt-Thieme Gerhard Backfried Norbert Pfannerer Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim Germany SAIL LABS Technology AG Vienna Austria

Usually, in intelligent tutoring systems the task sequencing is done by means of expert and domain knowledge. In a former work we presented a new efficient task sequencer without using the expensive expert and domain knowledge. This task sequencer only uses former performances and decides about the next task according to Vygotsky's Zone of Proximal Development, that is to neither bore nor frustrate the student. We aim to support this task sequencer by a further automatically to gain information, namely students affect recognized from his speech input. However, the collection of the data from children needed for training an affect recognizer in this field is challenging as it is costly and complex and one has to consider privacy issues carefully. These problems lead to small data sets and limited performances of classification methods. Hence, in this work we propose an approach for improving the affect recognition in intelligent tutoring systems, which uses a special structure of several support vector machines with different input feature vectors. Furthermore, we propose a new kind of features for this problem. Different experiments with two real data sets show, that our approach is able to improve the classification performance on average by 49% in comparison to using a single classifier.

关键词： Support vector machines Speech Speech recognition Feature extraction Vectors Artificial intelligence Speech processing

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Minimal Invasive Integration of learning Analytics Services in Intelligent Tutoring systems

Minimal Invasive Integration of Learning Analytics Services ...

引用

International Conference on Advanced learning Technologies (ICALT)

作者： Carlotta Schatten Martin Wistuba Lars Schmidt-Thieme Sergio Gutiérrez-Santos Universitat Hildesheim Hildesheim Niedersachsen DE Information Systems and Machine Learning Lab University of Hildesheim Germany Birbeck College London University UK

A common problem when trying to apply data mining techniques to improve educational systems is the disconnection between those who have the expertise (e.g. Universities) and those who have access to the data (e.g. Small companies). Bringing expertise into educational in-production systems is complicated because companies are reluctant to invest a lot of effort into integrating new technology that they do not fully trust, while the technology cannot prove its worth without access to real, valid data. In this paper we explore the requirements that machine learning systems have to be applied to specific learning problems (sequencing and performance prediction), and then propose a minimally invasive protocol for sequencing (based on web services) to easily integrate learning Analytics Services into e-learning systems.

关键词： Sequential analysis Data models Databases Load modeling Predictive models Computational modeling

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Move prediction in go-modelling feature interactions using latent factors

引用

36th Annual German Conference on Artificial Intelligence, KI 2013

作者： Wistuba, Martin Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Germany

ISBN: (纸本)9783642409417

Move prediction systems have always been part of strong Go programs. Recent research has revealed that taking interactions between features into account improves the performance of move predictions. In this paper, a factorization model is applied and a supervised learning algorithm, Latent Factor Ranking (LFR), which enables to consider these interactions, is introduced. Its superiority will be demonstrated in comparison to other state-of-the-art Go move predictors. LFR improves accuracy by 3% over current state-of-the-art Go move predictors on average and by 5% in the middle-and endgame of a game. Depending on the dimensionality of the shared, latent factor vector, an overall accuracy of over 41% is achieved. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Forecasting

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Supervised clustering of social media streams

Supervised clustering of social media streams

引用

2013 Multimedia Benchmark Workshop, MediaEval 2013

作者： Wistuba, Martin Schmidt-Thieme, Lars University of Hildesheim Information Systems and Machine Learning Lab. Germany

In this paper we present our approach for the Social Event Detection Task 1 of the MediaEval 2013. We address the problem of event detection and clustering by learning a distance measure between two images in a supervised way. Then, we apply a variant of the Quality Threshold clustering to detect events and assign the images accordingly. We can show that the performance measures do not decrease for an increasing number of documents and report the results achieved for the challenge.

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Supervised dimensionality reduction via nonlinear target estimation

Supervised dimensionality reduction via nonlinear target est...

引用

15th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2013

作者： Grabocka, Josif Drumond, Lucas Schmidt-Thieme, Lars Information Systems and Machine Learning Lab. Samelsonplatz 22 31141 Hildesheim Germany

ISBN: (纸本)9783642401305

Dimensionality reduction is a crucial ingredient of machine learning and data mining, boosting classification accuracy through the isolation of patterns via omission of noise. Nevertheless, recent studies have shown that dimensionality reduction can benefit from label information, via a joint estimation of predictors and target variables from a low-rank representation. In the light of such inspiration, we propose a novel dimensionality reduction which simultaneously reconstructs the predictors using matrix factorization and estimates the target variable via a dual-form maximum margin classifier from the latent space. The joint optimization function is learned through a coordinate descent algorithm via stochastic updates. Finally empirical results demonstrate the superiority of the proposed method compared to both classification in the original space (no reduction), or classification after unsupervised reduction. © 2013 Springer-Verlag GmbH.

关键词： Stochastic systems

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Efficient classification of long time-series

Efficient classification of long time-series

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4th ICT Innovations Conference on Secure and Intelligent systems

作者： Grabocka, Josif Bedalli, Erind Schmidt-Thieme, Lars Information Systems and Machine Learning Lab. University of Hildesheim Samelsonplatz 22 31141 Hildesheim Germany University of Tirana Tirana Albania University of Elbasan Elbasan Albania

ISBN: (纸本)9783642371684

Time-series classification has gained wide attention within the machine learning community, due to its large range of applicability varying from medical diagnosis, financial markets, up to shape and trajectory classification. The current state-of-art methods applied in time-series classification rely on detecting similar instances through neighboring algorithms. Dynamic Time Warping (DTW) is a similarity measure that can identify the similarity of two time-series, through the computation of the optimal warping alignment of time point pairs, therefore DTW is immune towards patterns shifted in time or distorted in size/shape. Unfortunately the classification time complexity of computing the DTW distance of two series is quadratic, subsequently DTW based nearest neighbor classification deteriorates to quartic order of time complexity per test set. The high time complexity order causes the classification of long time series to be practically infeasible. In this study we propose a fast linear classification complexity method. Our method projects the original data to a reduced latent dimensionality using matrix factorization, while the factorization is learned efficiently via stochastic gradient descent with fast convergence rates and early stopping. The latent data dimensionality is set to be as low as the cardinality of the label variable. Finally, Support Vector machines with polynomial kernels are applied to classify the reduced dimensionality data. Experimentations over long time series datasets from the UCR collection demonstrate the superiority of our method, which is orders of magnitude faster than baselines while being superior even in terms of classification accuracy. © 2013 Springer-Verlag.

关键词： Data mining

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HNNP - A Hybrid Neural Network Plait for Improving Image Classification with Additional Side information

HNNP - A Hybrid Neural Network Plait for Improving Image Cla...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Ruth Janning Carlotta Schatten Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim Hildesheim Germany

Most of the artificial intelligence and machine learning researches deal with big data today. However, there are still a lot of real world problems for which only small and noisy data sets exist. Hence, in this paper we focus on those small data sets of noisy images. Applying learning models to such data may not lead to the best possible results because of few and noisy training examples. We propose a hybrid neural network plait for improving the classification performance of state-of-the-art learning models applied to the images of such data sets. The improvement is reached by (1) using additionally to the images different further side information delivering different feature sets and requiring different learning models, (2) retraining all different learning models interactively within one common structure. The proposed hybrid neural network plait architecture reached in the experiments with 2 different data sets on average a classification performance improvement of 40% and 52% compared to a single convolutional neural network and 13% and 17% compared to a stacking ensemble method.

关键词： Artificial neural networks Biological neural networks Stacking Training Ground penetrating radar Multilayer perceptrons

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Factorized Decision Trees for Active learning in Recommender systems

Factorized Decision Trees for Active Learning in Recommender...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Rasoul Karimi Martin Wistuba Alexandros Nanopoulos Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) Samelsonplatz 1 University of Hildesheim Hildesheim Germany

A key challenge in recommender systems is how to profile new users. A well-known solution for this problem is to use active learning techniques and ask the new user to rate a few items to reveal her preferences. The sequence of queries should not be static, i.e in each step the best query depends on the responses of the new user to the previous queries. Decision trees have been proposed to capture the dynamic aspect of this process. In this paper we improve decision trees in two ways. First, we propose the Most Popular Sampling (MPS) method to increase the speed of the tree construction. In each node, instead of checking all candidate items, only those which are popular among users associated with the node are examined. Second, we develop a new algorithm to build decision trees. It is called Factorized Decision Trees (FDT) and exploits matrix factorization to predict the ratings at nodes of the tree. The experimental results on the Netflix dataset show that both contributions are successful. The MPS increases the speed of the tree construction without harming the accuracy. And FDT improves the accuracy of rating predictions especially in the last queries.

关键词： Decision trees Recommender systems Complexity theory Training Mathematical model Accuracy Equations

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