short-paper

Effective Trend Detection within a Dynamic Search Context

Authors:

Anat Hashavit,

Roy Levin,

Ido Guy,

Gilad KutielAuthors Info & Claims

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

Pages 817 - 820

https://doi.org/10.1145/2911451.2914705

Published: 07 July 2016 Publication History

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Abstract

In recent years, studies about trend detection in online social media streams have begun to emerge. Since not all users are likely to always be interested in the same set of trends, some of the research also focused on personalizing the trends by using some predefined personalized context.

In this paper, we take this problem further to a setting in which the user's context is not predefined, but rather determined as the user issues a query. This presents a new challenge since trends cannot be computed ahead of time using high latency algorithms. We present RT-Trend, an online trend detection algorithm that promptly finds relevant in-context trends as users issue search queries over a dataset of documents.

We evaluate our approach using real data from an online social network by assessing its ability to predict actual activity increase of social network entities in the context of a search result. Since we implemented this feature into an existing tool with an active pool of users, we also report click data, which suggests positive feedback.

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Cited By

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Al-Ghezi AWiese L(2024)Analyzing workload trends for boosting triple stores performanceInformation Systems10.1016/j.is.2024.102420125:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.is.2024.102420
Al-Ghezi AWiese L(2022)Analysing Workload Trends for Boosting Triple Stores PerformanceAdvances in Databases and Information Systems10.1007/978-3-031-15740-0_21(285-298)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-15740-0_21
Shu KLi LWang SZhou YLiu H(2020)Joint Local and Global Sequence Modeling in Temporal Correlation Networks for Trending Topic DetectionProceedings of the 12th ACM Conference on Web Science10.1145/3394231.3397924(335-344)Online publication date: 6-Jul-2020
https://dl.acm.org/doi/10.1145/3394231.3397924

Index Terms

Effective Trend Detection within a Dynamic Search Context
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Data streams
        Temporal data
  2. Information retrieval

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Published In

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

July 2016

1296 pages

ISBN:9781450340694

DOI:10.1145/2911451

General Chairs:
Raffaele Perego
ISTI-CNR, Italy
,
Fabrizio Sebastiani
Qatar Computing Research Institute, HBKU, Qatar
,
Program Chairs:
Javed Aslam
Northeastern University, US
,
Ian Ruthven
University of Strathclyde, UK
,
Justin Zobel
University of Melbourne, Australia

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 07 July 2016

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SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval

July 17 - 21, 2016

Pisa, Italy

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SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Al-Ghezi AWiese L(2024)Analyzing workload trends for boosting triple stores performanceInformation Systems10.1016/j.is.2024.102420125:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.is.2024.102420
Al-Ghezi AWiese L(2022)Analysing Workload Trends for Boosting Triple Stores PerformanceAdvances in Databases and Information Systems10.1007/978-3-031-15740-0_21(285-298)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-15740-0_21
Shu KLi LWang SZhou YLiu H(2020)Joint Local and Global Sequence Modeling in Temporal Correlation Networks for Trending Topic DetectionProceedings of the 12th ACM Conference on Web Science10.1145/3394231.3397924(335-344)Online publication date: 6-Jul-2020
https://dl.acm.org/doi/10.1145/3394231.3397924
Epure EKille BIngvaldsen JDeneckere RSalinesi CAlbayrak SCremonesi PRicci FBerkovsky STuzhilin A(2017)Recommending Personalized News in Short User SessionsProceedings of the Eleventh ACM Conference on Recommender Systems10.1145/3109859.3109894(121-129)Online publication date: 27-Aug-2017
https://dl.acm.org/doi/10.1145/3109859.3109894

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Expediting search trend detection via prediction of query counts

Emerging trends in search user interfaces

Dynamic vocabularies for web-based concept detection by trend discovery

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