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PeARS: a Peer-to-peer Agent for Reciprocated Search

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Aurelie HerbelotAuthors Info & Claims

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

Pages 41 - 42

https://doi.org/10.1145/2872518.2889369

Published: 11 April 2016 Publication History

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Abstract

This paper presents PeARS (Peer-to-peer Agent for Reciprocated Search), an algorithm for distributed Web search that emulates the offline behaviour of a human with an information need. Specifically, the algorithm models the process of 'calling a friend', i.e. directing one's query to the knowledge holder most likely to answer it. The system allows network users to index and share part of their browsing history in a way that makes them 'experts' on some topics. A layer of distributional semantics agents then performs targeted information retrieval in response to user queries, in a fully automated fashion.

References

[1]

Marco Baroni, Georgiana Dinu, and Germán Kruszewski. Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In Proceedings of ACL, pages 238--247, 2014.

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[2]

Katrin Erk. Vector space models of word meaning and phrase meaning: A survey. Language and Linguistics Compass, 6(10):635--653, 2012.

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[3]

Yi Yang, Wen-tau Yih, and Christopher Meek. WIKIQA: A Challenge Dataset for Open-Domain Question Answering. In Proceedings of EMNLP, 2015.

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Index Terms

PeARS: a Peer-to-peer Agent for Reciprocated Search
1. Information systems
  1. Information retrieval
    1. Search engine architectures and scalability
      1. Distributed retrieval
      2. Peer-to-peer retrieval

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Published In

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

April 2016

1094 pages

ISBN:9781450341448

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 11 April 2016

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WWW '16

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IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

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WWW '16 Companion Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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A Parameterized Approach to Spam-Resilient Link Analysis of the Web

A machine learning approach for improved BM25 retrieval

SSW: A Small-World-Based Overlay for Peer-to-Peer Search

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