NDLI: Exploiting correlated keywords to improve approximate information filtering

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Exploiting correlated keywords to improve approximate information filtering

Content Provider	ACM Digital Library
Author	Weikum, Gerhard Zimmer, Christian Tryfonopoulos, Christos
Abstract	Information filtering, also referred to as publish/subscribe, complements one-time searching since users are able to subscribe to information sources and be notified whenever new documents of interest are published. In approximate information filtering only selected information sources, that are likely to publish documents relevant to the user interests in the future, are monitored. To achieve this functionality, a subscriber exploits statistical metadata to identify promising publishers and index its continuous query only in those publishers. The statistics are maintained in a directory, usually on a per-keyword basis, thus disregarding possible correlations among keywords. Using this coarse information, poor publisher selection may lead to poor filtering performance and thus loss of interesting $documents.^{1}$ Based on the above observation, this work extends query routing techniques from the domain of distributed information retrieval in peer-to-peer (P2P) networks, and provides new algorithms for exploiting the correlation among keywords in a filtering setting. We develop and evaluate two algorithms based on single-key and multi-key statistics and utilize two different synopses (Hash Sketches and KMV synopses) to compactly represent publishers. Our experimental evaluation using two real-life corpora with web and blog data demonstrates the filtering effectiveness of both approaches and highlights the different tradeoffs.
Starting Page	323
Ending Page	330
Page Count	8
File Format	PDF
ISBN	9781605581644
DOI	10.1145/1390334.1390390
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Approximate publish/subscribe Distinct-value (dv) estimation Distributed information filtering (if) Peer-to-peer (p2p) Information systems
Content Type	Text
Resource Type	Article

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