Improving Category Specific Web Search by Learning Query Modifications

Users looking for documents within specific categories may have a difficult time locating valuable documents using general purpose search engines. We present an automated method for learning query modifications that can dramatically improve precision for locating pages within specified categories using web search engines. We also present a classification procedure that can recognize pages in a specific category with high precision, using textual content, text location, and HTML structure. Evaluation shows that the approach is highly effective for locating personal homepages and calls for papers. These algorithms are used to improve category specific search in the Inquirus 2 search engine. Typical web search engines index millions of pages across a variety of categories, and return results ranked by expected topical relevance. Only a small percentage of these pages may be of a specific category, for example, personal homepages, or calls for papers. A user may examine large numbers of pages about the right topic, but not of the desired category. In this paper, we describe a methodology for category-specific web search. We use a classifier to recognize web pages of a specific category and learn modifications to queries that bias results toward documents in that category. Using this approach, we have developed metasearch tools to effectively retrieve documents in several categories, including personal homepages, calls for papers, research papers, product reviews, and guide or FAQ documents. For a specific category, our first step is to train a support vector machine (SVM) [16] to classify pages by membership in the desired category. Performance is improved by considering, in addition to words and phrases, the documents’ HTML structure and simple word location information (e.g., whether a word appears near the top of the document). Second, we learn a set of query modifications. For this experiment, a query modification is a set of extra words or phrases added to a user query to increase the likelihood that results of the desired category are ranked near the top.1 Since not all search engines respond the same way to modifications, we use our classifier to automatically evaluate the results from each search engine, and produce a ranking of search engine and query modification pairs. This approach compensates for differences between performance on the training set and the search engine, which has a larger database and unknown ordering policy.