About Trey

Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning and data mining. The crucial limitation of those models, however, is their 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, in which each of them has a large set of possible values. In the big data era, one could 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 may 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, unrestricted Bayesian Networks may become infeasible for representing the probability distributions. In this paper, we introduce an extension to Bayesian Networks that can handle massive sets of hierarchical data in a reasonable amount of time and space. The proposed model achieves high precision and high recall when used as a 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 among search keywords with high accuracy.

Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who interacted with or rated the target item (collaborative filtering). To combine these two filtering approaches, current model-based hybrid recommendation systems typically require extensive feature engineering to construct a user profile. Statistical Relational Learning (SRL) provides a straightforward way to combine the two approaches. However, due to the large scale of the data used in real world recommendation systems, little research exists on applying SRL models to hybrid recommendation systems, and essentially none of that research has been applied on real big-data-scale systems. In this paper, we proposed a way to adapt the state-of-the-art in SRL learning approaches to construct a real hybrid recommendation system. Furthermore, in order to satisfy a common requirement in recommendation systems (i.e. that false positives are more undesirable and therefore penalized more harshly than false negatives), our approach can also allow tuning the tradeoff between the precision and recall of the system in a principled way. Our experimental results demonstrate the efficiency of our proposed approach as well as its improved performance on recommendation precision

As the ability to store and process massive amounts of user behavioral data increases, new approaches continue to arise for leveraging the wisdom of the crowds to gain insights that were previously very challenging to discover by text mining alone. For example, through collaborative filtering, we can learn previously hidden relationships between items based upon users' interactions with them, and we can also perform ontology mining to learn which keywords are semantically-related to other keywords based upon how they are used together by similar users as recorded in search engine query logs. The biggest challenge to this collaborative filtering approach is the variety of noise and outliers present in the underlying user behavioral data. In this paper we propose a novel approach to improve the quality of semantic relationships extracted from user behavioral data. Our approach utilizes millions of documents indexed into an inverted index in order to detect and remove noise and outliers.

Common difficulties like the cold-start problem and a lack of sufficient information about users due to their limited interactions have been major challenges for most recommender systems (RS). To overcome these challenges and many similar ones that result in low accuracy (precision and recall) recommendations, we propose a novel system that extracts semantically-related search keywords based on the aggregate behavioral data of many users. These semantically-related search keywords can be used to substantially increase the amount of knowledge about a speciffic user's interests based upon even a few searches and thus improve the accuracy of the RS. The proposed system is capable of mining aggregate user search logs to discover semantic relationships between key phrases in a manner that is language agnostic, human understandable, and virtually noise-free. These semantically related keywords are obtained by looking at the links between queries of similar users which, we believe, represent a largely untapped source for discovering latent semantic relationships between search terms.