Results 1  10
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110
Gaussian processes for ordinal regression
 Journal of Machine Learning Research
, 2004
"... We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation propagation ..."
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Cited by 116 (4 self)
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We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation propagation algorithm respectively, are derived for hyperparameter learning and model selection. We compare these two Gaussian process approaches with a previous ordinal regression method based on support vector machines on some benchmark and realworld data sets, including applications of ordinal regression to collaborative filtering and gene expression analysis. Experimental results on these data sets verify the usefulness of our approach.
A Review of Kernel Methods in Machine Learning
, 2006
"... We review recent methods for learning with positive definite kernels. All these methods formulate learning and estimation problems as linear tasks in a reproducing kernel Hilbert space (RKHS) associated with a kernel. We cover a wide range of methods, ranging from simple classifiers to sophisticate ..."
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Cited by 95 (4 self)
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We review recent methods for learning with positive definite kernels. All these methods formulate learning and estimation problems as linear tasks in a reproducing kernel Hilbert space (RKHS) associated with a kernel. We cover a wide range of methods, ranging from simple classifiers to sophisticated methods for estimation with structured data.
Multiple aspect ranking using the good grief algorithm
 In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLTNAACL
, 2007
"... We address the problem of analyzing multiple related opinions in a text. For instance, in a restaurant review such opinions may include food, ambience and service. We formulate this task as a multiple aspect ranking problem, where the goal is to produce a set of numerical scores, one for each aspect ..."
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Cited by 84 (9 self)
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We address the problem of analyzing multiple related opinions in a text. For instance, in a restaurant review such opinions may include food, ambience and service. We formulate this task as a multiple aspect ranking problem, where the goal is to produce a set of numerical scores, one for each aspect. We present an algorithm that jointly learns ranking models for individual aspects by modeling the dependencies between assigned ranks. This algorithm guides the prediction of individual rankers by analyzing metarelations between opinions, such as agreement and contrast. We prove that our agreementbased joint model is more expressive than individual ranking models. Our empirical results further confirm the strength of the model: the algorithm provides significant improvement over both individual rankers and a stateoftheart joint ranking model. 1
Tagaware recommender systems by fusion of collaborative filtering algorithms
 In Proceedings of the 2nd ACM Symposium on Applied Computing
, 1995
"... Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user and itembased methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content informa ..."
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Cited by 84 (3 self)
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Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user and itembased methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are “global ” descriptions of items, tags are “local ” descriptions of items given by the users. To the best of our knowledge, there hasn’t been any prior study on tagaware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the threedimensional correlations to three twodimensional correlations and then applying a fusion method to reassociate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with reallife data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.
Multiverse recommendation: ndimensional tensor factorization for contextaware collaborative filtering
 In Proceedings of the fourth ACM conference on Recommender systems
, 2010
"... Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most modelbased Collaborative Filtering approaches such as Matrix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we intro ..."
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Cited by 77 (4 self)
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Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most modelbased Collaborative Filtering approaches such as Matrix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factorization that allows for a flexible and generic integration of contextual information by modeling the data as a UserItemContext Ndimensional tensor instead of the traditional 2D UserItem matrix. In the proposed model, called Multiverse Recommendation, different types of context are considered as additional dimensions in the representation of the data as a tensor. The factorization of this tensor leads to a compact model of the data which can be used to provide contextaware recommendations. We provide an algorithm to address the Ndimensional factorization, and show that the Multiverse Recommendation improves upon noncontextual Matrix Factorization up to 30 % in terms of the Mean Absolute Error (MAE). We also compare to two stateoftheart contextaware methods and show that Tensor Factorization consistently outperforms them both in semisynthetic and realworld data – improvements range from 2.5 % to more than 12 % depending on the data. Noticeably, our approach outperforms other methods by a wider margin whenever more contextual information is available.
Bundle Methods for Regularized Risk Minimization
"... A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different regularizers. Examples include linear Support Vector Machines (SVMs), Gaussian Processes, Logistic Regression, Conditional ..."
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Cited by 76 (4 self)
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A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different regularizers. Examples include linear Support Vector Machines (SVMs), Gaussian Processes, Logistic Regression, Conditional Random Fields (CRFs), and Lasso amongst others. This paper describes the theory and implementation of a scalable and modular convex solver which solves all these estimation problems. It can be parallelized on a cluster of workstations, allows for datalocality, and can deal with regularizers such as L1 and L2 penalties. In addition to the unified framework we present tight convergence bounds, which show that our algorithm converges in O(1/ɛ) steps to ɛ precision for general convex problems and in O(log(1/ɛ)) steps for continuously differentiable problems. We demonstrate the performance of our general purpose solver on a variety of publicly available datasets.
Effective Missing Data Prediction for Collaborative Filtering
, 2007
"... Memorybased collaborative filtering algorithms have been widely adopted in many popular recommender systems, although these approaches all suffer from data sparsity and poor prediction quality problems. Usually, the useritem matrix is quite sparse, which directly leads to inaccurate recommendation ..."
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Cited by 50 (12 self)
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Memorybased collaborative filtering algorithms have been widely adopted in many popular recommender systems, although these approaches all suffer from data sparsity and poor prediction quality problems. Usually, the useritem matrix is quite sparse, which directly leads to inaccurate recommendations. This paper focuses the memorybased collaborative filtering problems on two crucial factors: (1) similarity computation between users or items and (2) missing data prediction algorithms. First, we use the enhanced Pearson Correlation Coefficient (PCC) algorithm by adding one parameter which overcomes the potential decrease of accuracy when computing the similarity of users or items. Second, we propose an effective missing data prediction algorithm, in which information of both users and items is taken into account. In this algorithm, we set the similarity threshold for users and items respectively, and the prediction algorithm will determine whether predicting the missing data or not. We also address how to predict the missing data by employing a combination of user and item information. Finally, empirical studies on dataset MovieLens have shown that our newly proposed method outperforms other stateoftheart collaborative filtering algorithms and it is more robust against data sparsity.
Stochastic relational models for discriminative link prediction
 Advances in Neural Information Processing Systems
, 2007
"... We introduce a Gaussian process (GP) framework, stochastic relational models (SRM), for learning social, physical, and other relational phenomena where interactions between entities are observed. The key idea is to model the stochastic structure of entity relationships (i.e., links) via a tensor int ..."
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Cited by 49 (18 self)
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We introduce a Gaussian process (GP) framework, stochastic relational models (SRM), for learning social, physical, and other relational phenomena where interactions between entities are observed. The key idea is to model the stochastic structure of entity relationships (i.e., links) via a tensor interaction of multiple GPs, each defined on one type of entities. These models in fact define a set of nonparametric priors on infinite dimensional tensor matrices, where each element represents a relationship between a tuple of entities. By maximizing the marginalized likelihood, information is exchanged between the participating GPs through the entire relational network, so that the dependency structure of links is messaged to the dependency of entities, reflected by the adapted GP kernels. The framework offers a discriminative approach to link prediction, namely, predicting the existences, strengths, or types of relationships based on the partially observed linkage network as well as the attributes of entities (if given). We discuss properties and variants of SRM and derive an efficient learning algorithm. Very encouraging experimental results are achieved on a toy problem and a usermovie preference link prediction task. In the end we discuss extensions of SRM to general relational learning tasks. 1
Support vector ordinal regression
 Neural Computation
, 2007
"... In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these opt ..."
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Cited by 39 (2 self)
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In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The SMO algorithm is adapted for the resulting optimization problems; it is extremely easy to implement and scales efficiently as a quadratic function of the number of examples. The results of numerical experiments on some benchmark and realworld data sets, including applications of ordinal regression to information retrieval and collaborative filtering, verify the usefulness of these approaches. 1
S.: Combining eye movements and collaborative filtering for proactive information retrieval
 In: Proc. SIGIR
, 2005
"... We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative filtering. We have constructed a controlled experimental setting, a prototype application, in which the users try to find interesting scientific articles by browsing their titles. Impli ..."
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Cited by 34 (15 self)
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We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative filtering. We have constructed a controlled experimental setting, a prototype application, in which the users try to find interesting scientific articles by browsing their titles. Implicit feedback is inferred from eye movement signals, with discriminative hidden Markov models estimated from existing data in which explicit relevance feedback is available. Collaborative filtering is carried out using the User Rating Profile model, a stateoftheart probabilistic latent variable model, computed using Markov Chain Monte Carlo techniques. For new document titles the prediction accuracy with eye movements, collaborative filtering, and their combination was significantly better than by chance. The best