Ranking (information retrieval)

About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.

ProFusion*: Intelligent Fusion from Multiple, Distributed Search Engines 1

Abstract: The explosive growth of the World Wide Web, and the resulting information overload, has led to a mini-explosion in World Wide Web search engines. This mini-explosion, in turn, led to the development of ProFusion, a meta search engine. Educators, like other users, do not have the time to evaluate multiple search engines to knowledgeably select the best for their uses. Nor do they have the time to submit each query to multiple search engines and wade through the resulting flood of good information, duplicated information, irrelevant information, and missing documents. ProFusion sends user queries to multiple underlying search engines in parallel, retrieves and merges the resulting URLs. It identifies and removes duplicates and creates one relevance-ranked list. If desired, the actual documents can be pre-fetched to remove yet more duplicates and broken links. ProFusion's performance has been compared to the individual search engines and other meta searchers, demonstrating its ability to retrieve more relevant information and present fewer duplicates pages. The system can automatically analyze queries to identify its topic(s) and, based on that analysis, select the most appropriate search engines for the query.

Statistical Analysis of Bayes Optimal Subset Ranking

Abstract: The ranking problem has become increasingly important in modern applications of statistical methods in automated decision making systems. In particular, we consider a formulation of the statistical ranking problem which we call subset ranking, and focus on the discounted cumulated gain (DCG) criterion that measures the quality of items near the top of the rank-list. Similar to error minimization for binary classification, direct optimization of natural ranking criteria such as DCG leads to a nonconvex optimization problems that can be NP-hard. Therefore, a computationally more tractable approach is needed. We present bounds that relate the approximate optimization of DCG to the approximate minimization of certain regression errors. These bounds justify the use of convex learning formulations for solving the subset ranking problem. The resulting estimation methods are not conventional, in that we focus on the estimation quality in the top-portion of the rank-list. We further investigate the asymptotic statistical behavior of these formulations. Under appropriate conditions, the consistency of the estimation schemes with respect to the DCG metric can be derived.

A cluster-based resampling method for pseudo-relevance feedback

Abstract: Typical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling method to select better pseudo-relevant documents based on the relevance model. The main idea is to use document clusters to find dominant documents for the initial retrieval set, and to repeatedly feed the documents to emphasize the core topics of a query. Experimental results on large-scale web TREC collections show significant improvements over the relevance model. For justification of the resampling approach, we examine relevance density of feedback documents. A higher relevance density will result in greater retrieval accuracy, ultimately approaching true relevance feedback. The resampling approach shows higher relevance density than the baseline relevance model on all collections, resulting in better retrieval accuracy in pseudo-relevance feedback. This result indicates that the proposed method is effective for pseudo-relevance feedback.

Research mode for a knowledge base search and retrieval system

Abstract: A research mode in a search and retrieval system generates a research document that infers an answer to a query from multiple documents. The search and retrieval system includes point of view gists for documents to provide a synopsis for a corresponding document with a slant toward a topic. To generate a research document, the search and retrieval system processes a query to identify one or more topics related to the query, selects document themes relevant to the query, and then selects point of view gists, based on the document themes, that have a slant towards the topics related to the query. A knowledge base, which includes categories arranged hierarchically, is configured as a directed graph to links those categories having a lexical, semantic or usage association. Through use of the knowledge base, an expanded set of query terms are generated, and research documents are compiled that include point of view gists relevant to the expanded set of query terms. A content processing system, which identifies the themes for a document and classifies the document themes in categories of the knowledge base, is also disclosed.