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Evaluating Recommendation Systems
TLDR
This paper discusses how to compare recommenders based on a set of properties that are relevant for the application, and focuses on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms. Expand
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
TLDR
This paper reviews the proper construction of offline experiments for deciding on the most appropriate algorithm, and discusses three important tasks of recommender systems, and classify a set of appropriate well known evaluation metrics for each task. Expand
Hidden conditional random fields for phone classification
TLDR
This paper presents the results on the TIMIT phone classification task and shows that HCRFs outperforms comparable ML and CML/MMI trained HMMs and has the ability to handle complex features without any change in training procedure. Expand
Convergence Theorems for Generalized Alternating Minimization Procedures
TLDR
This work studies EM variants in which the E-step is not performed exactly, either to obtain improved rates of convergence, or due to approximations needed to compute statistics under a model family over which E-steps cannot be realized. Expand
Evaluating Recommender Systems
TLDR
This paper discusses how to compare recommenders based on a set of properties that are relevant for the application, and focuses on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms. Expand
A Model for Temporal Dependencies in Event Streams
TLDR
This work introduces the Piecewise-Constant Conditional Intensity Model, a model for learning temporal dependencies in event streams, and describes an importance sampling algorithm for forecasting future events using these models, using a proposal distribution based on Poisson superposition. Expand
Discriminative speaker adaptation with conditional maximum likelihood linear regression
We present a simplified derivation of the extended Baum-Welch procedure, which shows that it can be used for Maximum Mutual Information (MMI) of a large class of continuous emission density hiddenExpand
Usability guided key-target resizing for soft keyboards
TLDR
An anchored key-target method is proposed which incorporates usability principles so that soft keyboards can remain robust to errors while respecting usability principles, and it is found that using anchored dynamic key-targets significantly reduce keystroke errors as compared to the state of the art. Expand
Tied boltzmann machines for cold start recommendations
TLDR
A novel statistical model, the tied Boltzmann machine, for combining collaborative and content information for recommendations, whose parameters are constrained according to the content associated with the items. Expand
Training Algorithms for Hidden Conditional Random Fields
TLDR
Algorithms for training hidden conditional random fields (HCRFs) - a class of direct models with hidden state sequences - are investigated, and a new scheme for model flattening is proposed, and it is compared to the state of the art. Expand
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