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The primary business model behind Web search is based on textual advertising, where contextually relevant ads are displayed alongside search results. We address the problem of selecting these ads so that they are both relevant to the queries and profitable to the search engine, showing that optimizing ad relevance and revenue is not equivalent. Selecting(More)
Sponsored search systems are tasked with matching queries to relevant advertisements. The current state-of-the-art matching algorithms expand the user's query using a variety of external resources, such as Web search results. While these expansion-based algorithms are highly effective, they are largely inefficient and cannot be applied in real-time. In(More)
The business of Web search, a $10 billion industry, relies heavily on <i>sponsored search</i>, whereas a few carefully-selected paid advertisements are displayed alongside algorithmic search results. A key technical challenge in sponsored search is to select ads that are relevant for the user's query. Identifying relevant ads is challenging because queries(More)
Understanding what interests and delights users is critical to effective behavioral targeting, especially in information-poor contexts. As users interact with content and advertising, their passive behavior can reveal their interests towards advertising. Two issues are critical for building effective targeting methods: what metric to optimize for and how to(More)
Our project is an application of machine learning technology to text classification on United States patents to automatically differentiate between patents relating to the biotech industry and those unrelated. Additionally, we attempted to further divide biotech patents into various groups, using k-means and Chi-Squared to automatically identify potentially(More)
Folksonomies allow users to collaboratively tag a variety of tex-tual and multimedia objects with sets of labels. The largest folk-sonomy projects, such as FLICKR and DEL.ICIO.US, contain millions of multi-labeled objects, and embed significant amounts of human knowledge. We propose a method for automatically using this knowledge to augment traditional IR(More)
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