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Dive into the research topics where Christoph Freudenthaler is active.

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Featured researches published by Christoph Freudenthaler.


international world wide web conferences | 2010

Factorizing personalized Markov chains for next-basket recommendation

Steffen Rendle; Christoph Freudenthaler; Lars Schmidt-Thieme

Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn the general taste of a user by factorizing the matrix over observed user-item preferences. On the other hand, MC methods model sequential behavior by learning a transition graph over items that is used to predict the next action based on the recent actions of a user. In this paper, we present a method bringing both approaches together. Our method is based on personalized transition graphs over underlying Markov chains. That means for each user an own transition matrix is learned - thus in total the method uses a transition cube. As the observations for estimating the transitions are usually very limited, our method factorizes the transition cube with a pairwise interaction model which is a special case of the Tucker Decomposition. We show that our factorized personalized MC (FPMC) model subsumes both a common Markov chain and the normal matrix factorization model. For learning the model parameters, we introduce an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data. Empirically, we show that our FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization.


conference on recommender systems | 2011

MyMediaLite: a free recommender system library

Zeno Gantner; Steffen Rendle; Christoph Freudenthaler; Lars Schmidt-Thieme

MyMediaLite is a fast and scalable, multi-purpose library of recommender system algorithms, aimed both at recommender system researchers and practitioners. It addresses two common scenarios in collaborative filtering: rating prediction (e.g. on a scale of 1 to 5 stars) and item prediction from positive-only implicit feedback (e.g. from clicks or purchase actions). The library offers state-of-the-art algorithms for those two tasks. Programs that expose most of the librarys functionality, plus a GUI demo, are included in the package. Efficient data structures and a common API are used by the implemented algorithms, and may be used to implement further algorithms. The API also contains methods for real-time updates and loading/storing of already trained recommender models. MyMediaLite is free/open source software, distributed under the terms of the GNU General Public License (GPL). Its methods have been used in four different industrial field trials of the MyMedia project, including one trial involving over 50,000 households.


international acm sigir conference on research and development in information retrieval | 2011

Fast context-aware recommendations with factorization machines

Steffen Rendle; Zeno Gantner; Christoph Freudenthaler; Lars Schmidt-Thieme

The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So far, the best performing method for context-aware rating prediction in terms of predictive accuracy is Multiverse Recommendation based on the Tucker tensor factorization model. However this method has two drawbacks: (1) its model complexity is exponential in the number of context variables and polynomial in the size of the factorization and (2) it only works for categorical context variables. On the other hand there is a large variety of fast but specialized recommender methods which lack the generality of context-aware methods. We propose to apply Factorization Machines (FMs) to model contextual information and to provide context-aware rating predictions. This approach results in fast context-aware recommendations because the model equation of FMs can be computed in linear time both in the number of context variables and the factorization size. For learning FMs, we develop an iterative optimization method that analytically finds the least-square solution for one parameter given the other ones. Finally, we show empirically that our approach outperforms Multiverse Recommendation in prediction quality and runtime.


international conference on data mining | 2010

Learning Attribute-to-Feature Mappings for Cold-Start Recommendations

Zeno Gantner; Lucas Drumond; Christoph Freudenthaler; Steffen Rendle; Lars Schmidt-Thieme

Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such cases, additional information about users (user attributes, e.g. gender, age, geographical location, occupation) and items (item attributes, e.g. genres, product categories, keywords) must be used. We describe a method that maps such entity (e.g. user or item) attributes to the latent features of a matrix (or higher-dimensional) factorization model. With such mappings, the factors of a MF model trained by standard techniques can be applied to the new-user and the new-item problem, while retaining its advantages, in particular speed and predictive accuracy. We use the mapping concept to construct an attribute-aware matrix factorization model for item recommendation from implicit, positive-only feedback. Experiments on the new-item problem show that this approach provides good predictive accuracy, while the prediction time only grows by a constant factor.


international conference on tools with artificial intelligence | 2011

Towards Optimal Active Learning for Matrix Factorization in Recommender Systems

Rasoul Karimi; Christoph Freudenthaler; Alexandros Nanopoulos; Lars Schmidt-Thieme

Recommender systems help web users to address information overload. However their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any rating. To address this problem, active learning methods have been proposed to acquire those ratings from users, that will help most in determining their interests. The optimal active learning selects a query that directly optimizes the expected error for the test data. This approach is applicable for prediction models in which this question can be answered in closed-form given the distribution of test data is known. Unfortunately, there are many tasks and models for which the optimal selection cannot efficiently be found in closed-form. Therefore, most of the active learning methods optimize different, non-optimal criteria, such as uncertainty. Nevertheless, in this paper we exploit the characteristics of matrix factorization, which leads to a closed-form solution and by being inspired from existing optimal active learning for the regression task, develop a method that approximates the optimal solution for recommender systems. Our results demonstrate that the proposed method improves the prediction accuracy of MF.


information reuse and integration | 2011

Non-myopic active learning for recommender systems based on Matrix Factorization

Rasoul Karimi; Christoph Freudenthaler; Alexandros Nanopoulos; Lars Schmidt-Thieme

Recommender systems help Web users to address information overload. However, their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any ratings. In this paper, we consider the new user problem as an optimization problem and propose a non-myopic active learning method to select items to be queried from the new user. The proposed method is based on Matrix Factorization (MF) which is a strong prediction model for recommender systems. First, the proposed method explores the latent space to get closer to the optimal new user parameters. Then, it exploits the learned parameters and slightly adjusts them. The results show that beside improving the accuracy of recommendation, MF approach also results in drastically reduced user waiting times, i.e., the time that the users wait before being asked a new query. Therefore, it is an ideal choice for using active learning in real-world applications of recommender systems.


computational intelligence and data mining | 2011

Active learning for aspect model in recommender systems

Rasoul Karimi; Christoph Freudenthaler; Alexandros Nanopoulos; Lars Schmidt-Thieme

Recommender systems help Web users to address information overload. Their performance, however, depends on the amount of information that users provide about their preferences. Users are not willing to provide information for a large amount of items, thus the quality of recommendations is affected specially for new users. Active learning has been proposed in the past, to acquire preference information from users. Based on an underlying prediction model, these approaches determine the most informative item for querying the new user to provide a rating. In this paper, we propose a new active learning method which is developed specially based on aspect model features. There is a difference between classic active learning and active learning for recommender system. In the recommender system context, each item has already been rated by training users while in classic active learning there is not training user. We take into account this difference and develop a new method which competes with a complicated bayesian approach in accuracy while results in drastically reduced (one order of magnitude) user waiting times, i.e., the time that the users wait before being asked a new query.


NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics | 2011

Factorizing Markov Models for Categorical Time Series Prediction

Christoph Freudenthaler; Steffen Rendle; Lars Schmidt-Thieme

During the last decade, recommender systems became a popular class of models for many commercial websites. One of the best state‐of‐the‐art methods for recommender systems are Matrix and Tensor Factorization models. Besides, Markov Chain models are common for representing sequential data problems (e.g. categorical time series data). The item recommendation problem of recommender systems in fact is a categorical time series problem where each user represents an individual categorical time series. In this paper we combine factorization models with Markov Chain models. To increase efficiency of parameter estimation we introduce our generalized Factorized Markov Chain model.


pacific-asia conference on knowledge discovery and data mining | 2014

Collective Matrix Factorization of Predictors, Neighborhood and Targets for Semi-supervised Classification

Lucas Drumond; Lars Schmidt-Thieme; Christoph Freudenthaler; Artus Krohn-Grimberghe

Due to the small size of available labeled data for semi-supervised learning, approaches to this problem make strong assumptions about the data, performing well only when such assumptions hold true. However, a lot of effort may have to be spent in understanding the data so that the most suitable model can be applied. This process can be as critical as gathering labeled data. One way to overcome this hindrance is to control the contribution of different assumptions to the model, rendering it capable of performing reasonably in a wide range of applications. In this paper we propose a collective matrix factorization model that simultaneously decomposes the predictor, neighborhood and target matrices (PNT-CMF) to achieve semi-supervised classification. By controlling how strongly the model relies on different assumptions, PNT-CMF is able to perform well on a wider variety of datasets. Experiments on synthetic and real world datasets show that, while state-of-the-art models (TSVM and LapSVM) excel on datasets that match their characteristics and have a performance drop on the others, our approach outperforms them being consistently competitive in different situations.


user centric media | 2009

Optimal Ranking for Video Recommendation

Zeno Gantner; Christoph Freudenthaler; Steffen Rendle; Lars Schmidt-Thieme

Item recommendation from implicit feedback is the task of predicting a personalized ranking on a set of items (e.g. movies, products, video clips) from user feedback like clicks or product purchases. We evaluate the performance of a matrix factorization model optimized for the new ranking criterion BPR-Opt on data from a BBC video web application. The experimental results indicate that our approach is superior to state-of-the-art models not directly optimized for personalized ranking.

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Zeno Gantner

University of Hildesheim

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Rasoul Karimi

University of Hildesheim

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Lucas Drumond

University of Hildesheim

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Andreas Quatember

Institute of Food and Agricultural Sciences

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