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Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this(More)
The explosion of user-generated content on the Web has led to new opportunities and significant challenges for companies, that are increasingly concerned about monitoring the discussion around their products. Tracking such discussion on weblogs, provides useful insight on how to improve products or market them more effectively. An important component of(More)
The diversity of an ensemble of classifiers is known to be an important factor in determining its generalization error. We present a new method for generating ensembles, Decorate (Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples), that directly constructs diverse hypotheses using additional artificially-constructed(More)
Ensemble methods like bagging and boosting that combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of the members of an ensemble is known to be an important factor in determining its generalization error. This paper presents a new method for generating ensembles that directly constructs(More)
Query by Committee is an effective approach to selective sampling in which disagreement amongst an ensemble of hypotheses is used to select data for labeling. Query by Bagging and Query by Boosting are two practical implementations of this approach that use Bagging and Boosting, respectively, to build the committees. For effective active learning, it is(More)
The goal of a Recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Suggestions for books on Amazon, or movies on Netflix, are real world examples of the operation of industry-strength recommender systems. The design of such recommendation engines depends on the domain and the(More)
Annotation acquisition is an essential step in training supervised classifiers. However, manual annotation is often time-consuming and expensive. The possibility of recruiting annotators through Internet services (e.g., Amazon Mechanic Turk) is an appealing option that allows multiple labeling tasks to be outsourced in bulk, typically with low overall costs(More)
When faced with the task of building accurate classifiers, active learning is often a beneficial tool for minimizing the requisite costs of human annotation. Traditional active learning schemes query a human for labels on intelligently chosen examples. However, human effort can also be expended in collecting alternative forms of annotation. For example, one(More)
The goal of sentiment prediction is to automatically identify whether a given piece of text expresses positive or negative opinion towards a topic of interest. One can pose sentiment prediction as a standard text categorization problem, but gathering labeled data turns out to be a bottleneck. Fortunately, background knowledge is often available in the form(More)