In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Ex Input:
*Correspondence: aljadda@uga.edu 1Department of Computer Science, University of Georgia, Athens,GA, USA Full list of author information is available at the end of the article †Equal contributor Abstract Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning and data mining. The crucial limitation of those models,however, is the scalability. The Bayesian Network, which is one of the most common PGMs used in machine learning and data mining, demonstrates this limitation when the training data consists of random variables, each of them has a large set of possible values. In the big data era, one would expect new extensions to the existing PGMs to handle the massive amount of data produced these days by computers, sensors and other electronic devices. With hierarchical data data that is arranged in a treelike structure with several levels one would expect to see hundreds of thousands or millions of values distributed over even just a small number of levels. When modeling this kind of hierarchical data across large data sets, Bayesian Networks become infeasible for representing the probability distributions. In this paper we introduce an extension to Bayesian Networks to handle massive sets of hierarchical data in a reasonable amount of time and space. The proposed model achieves perfect precision of 1.0 and high recall of 0.93 when it is used as multi-label classifier for the annotation of mass spectrometry data. On another data set of 1.5 billion search logs provided by CareerBuilder.com the model was able to predict latent semantic relationships between search keywords with accuracy up to 0.80.

Ex Output:
Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model


Ex Input:
Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRTbased proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student’s future response given previous responses using two publicly available and one proprietary data set. We find that IRT-based methods consistently matched or outperformed DKT across all data sets at the finest level of content granularity that was tractable for them to be trained on. A hierarchical extension of IRT that captured item grouping structure performed best overall. When data sets included non-trivial autocorrelations in student response patterns, a temporal extension of IRT improved performance over standard IRT while the RNNbased method did not. We conclude that IRT-based models provide a simpler, better-performing alternative to existing RNN-based models of student interaction data while also affording more interpretability and guarantees due to their formulation as Bayesian probabilistic models.

Ex Output:
Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation


Ex Input:
Collaborative filtering or recommender sys­ tems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, in­ cluding techniques based on correlation coef­ ficients, vector-based similarity calculations, and statistical Bayesian methods. We com­ pare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evalua­ tion metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second esti­ mates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommen­ dation in an ordered list. Experiments were run for datasets associ­ ated with 3 application areas, 4 experimen­ tal protocols, and the 2 evaluation met­ rics for the various algorithms. Results indicate that for a wide range of con­ ditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector­ similarity methods. Between correlation and Bayesian networks, the preferred method de­ pends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other con­ siderations include the size of database, speed of predictions, and learning time.

Ex Output:

Empirical Analysis of Predictive Algorithms for Collaborative Filtering