We present CiteSeer: an autonomous citation indexing system which indexes academic literature in electronic format (e.g. Postscript files on the Web). CiteSeer understands how to parse citations, identify citations to the same paper in different formats, and identify the context of citations in the body of articles. CiteSeer provides most of the advantages of traditional (manually constructed) citation indexes (e.g. the ISI citation indexes), including: literature retrieval by following citation links (e.g. by providing a list of papers that cite a given paper), the evaluation and ranking of papers, authors, journals, etc. based on the number of citations, and the identification of research trends. CiteSeer has many advantages over traditional citation indexes, including the ability to create more up-to-date databases which are not limited to a preselected set of journals or restricted by journal publication delays, completely autonomous operation with a corresponding reduction in cost, and powerful interactive browsing of the literature using the context of citations. Given a particular paper of interest, CiteSeer can display the context of how the paper is cited in subsequent publications. This context may contain a brief summary of the paper, another author's response to the paper, or subsequent work which builds upon the original article. CiteSeer allows the location of papers by keyword search or by citation links. Papers related to a given paper can be located using common citation information or word vector similarity. CiteSeer will soon be available for public use.

Recall and precision are often used to evaluate the effectiveness of information retrieval systems. They are easy to define if there is a single query and if the retrieval result generated for the query is a linear ordering. However, when the retrieval results are weakly ordered, in the sense that several documents have an identical retrieval status value with respect to a query, some probabilistic notion of precision has to be introduced. Relevance probability, expected precision, and so forth, are some alternatives mentioned in the literature for this purpose. Furthermore, when many queries are to be evaluated and the retrieval results averaged over these queries, some method of interpolation of precision values at certain preselected recall levels is needed. The currently popular approaches for handling both a weak ordering and interpolation are found to be inconsistent, and the results obtained are not easy to interpret. Moreover, in cases where some alternatives are available, no comparative analysis that would facilitate the selection of a particular strategy has been provided. In this paper, we systematically investigate the various problems and issues associated with the use of recall and precision as measures of retrieval system performance. Our motivation is to provide a comparative analysis of methods available for defining precision in a probabilistic sense and to promote a better understanding of the various issues involved in retrieval performance evaluation.

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In environments where exact synchronization between source data objects and cached copies is not achievable due to bandwidth or other resource constraints, stale (out-of-date) copies are permitted. It is desirable to minimize the overall divergence between source objects and cached copies by selectively refreshing modified objects. We call the online process of selecting which objects to refresh in order to minimize divergence best-effort synchronization. In most approaches to best-effort synchronization, the cache coordinates the process and selects objects to refresh. In this paper, we propose a best-effort synchronization scheduling policy that exploits cooperation between data sources and the cache. We also propose an implementation of our policy that incurs low communication overhead even in environments with very large numbers of sources. Our algorithm is adaptive to wide fluctuations in available resources and data update rates. Through experimental simulation over synthetic and real-world data, we demonstrate the effectiveness of our algorithm, and we quantify the significant decrease in divergence achievable with source cooperation.

The term weighting function known as IDF was proposed in 1972, and has since been extremely widely used, usually as part of a TF*IDF function. It is often described as a heuristic, and many papers have been written (some based on Shannon’s Information Theory) seeking to establish some theoretical basis for it. Some of these attempts are reviewed, and it is shown that the Information Theory approaches are problematic, but that there are good theoretical justifications of both IDF and TF*IDF in traditional probabilistic model of information retrieval.

Information retrieval systems are built to handle texts as topical items: texts are tabulated by occurrence frequencies of content words in them, under the assumption that text topic is reasonably well modeled by content word occurrence. But texts have several interesting characteristics beyond topic. The experiments described in this text investigate stylistic variation. Roughly put, style is the difference between two ways of saying the same thing -- and systematic stylistic variation can be used to characterize the genre of documents. These experiments investigate if stylistic information is distinguishable using simple language engineering methods, and if in that case this type of information can be used to improve information retrieval systems.

We introduce a system as part of the CiteSeer digital library project for automatic tracking of scientific literature that is relevant to a user’s research interests. Unlike previous systems that use simple keyword matching, CiteSeer is able to track and recommend topically relevant papers even when keyword based query profiles fail. This is made possible through the use of a heterogenous profile to represent user interests. These profiles include several representations, including content based relatedness measures. The CiteSeer tracking system is well integrated into the search and browsing facilities of CiteSeer, and provides the user with great flexibility in tuning a profile to better match his or her interests. The software for this system is available, and a sample database is online as a public service.

NeuroGrid is an adaptive decentralized search system. NeuroGrid nodes support distributed search through semantic routing forwarding of queries based on content), and a learning mechanism that dynamically adjusts metadata describing the contents of nodes and the files that make up those contents. NeuroGrid is an open-source project, and prototype software has been made available at http://www.neurogrid.net/ NeuroGrid presents users with an alternative to hierarchical, folder-based file organization, and in the process offers an alternative approach to distributed search.

The paper proposes a new learning mechanism to extract user preferences transparently for a World Wide Web recommender system. The general idea is that we use the entropy of the page being accessed to determine its interestingness based on its occurrence probability following a sequence of pages accessed by the user. The probability distribution of the pages is obtained by collecting the access patterns of users navigating on the Web. A finite context-model is used to represent the usage information. Based on our proposed model, we have developed an autonomous agent, named ProfBuilder, that works as an online recommender system for a Web site. ProfBuilder uses the usage information as a base for content-based and collaborative filtering.

Search engines are the primary gateways of information access on the Web today. Behind the scenes, search engines crawl the Web to populate a local indexed repository of Web pages, used to answer user search queries. In an aggregate sense, the Web is very dynamic, causing any repository of Web pages to become out of date over time, which in turn causes query answer quality to degrade. Given the considerable size, dynamicity, and degree of autonomy of the Web as a whole, it is not feasible for a search engine to maintain its repository exactly synchronized with the Web. In this paper we study how to schedule Web pages for selective (re)downloading into a search engine repository. The scheduling objective is to maximize the quality of the user experience for those who query the search engine. We begin with a quantitative characterization of the way in which the discrepancy between the content of the repository and the current content of the live Web impacts the quality of the user experience. This characterization leads to a usercentric metric of the quality of a search engine’s local repository. We use this metric to derive a policy for scheduling Web page (re)downloading that is driven by search engine usage and free of exterior tuning parameters. We then focus on the important subproblem of scheduling refreshing of Web pages already present in the repository, and show how to compute the priorities efficiently. We provide extensive empirical comparisons of our user-centric method against prior Web page refresh strategies, using real Web data. Our results demonstrate that our method requires far fewer resources to maintain same search engine quality level for users, leaving substantially more resources available for incorporating new Web pages into the search repository.