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CiteSight: supporting contextual citation recommendation using differential search

Published: 03 July 2014 Publication History

Abstract

A person often uses a single search engine for very different tasks. For example, an author editing a manuscript may use the same academic search engine to find the latest work on a particular topic or to find the correct citation for a familiar article. The author's tolerance for latency and accuracy may vary according to task. However, search engines typically employ a consistent approach for processing all queries. In this paper we explore how a range of search needs and expectations can be supported within a single search system using differential search. We introduce CiteSight, a system that provides personalized citation recommendations to author groups that vary based on task. CiteSight presents cached recommendations instantaneously for online tasks (e.g., active paper writing), and refines these recommendations in the background for offline tasks (e.g., future literature review). We develop an active cache-warming process to enhance the system as the author works, and context-coupling, a technique for augment sparse citation networks. By evaluating the quality of the recommendations and collecting user feedback, we show that differential search can provide a high level of accuracy for different tasks on different time scales. We believe that differential search can be used in many situations where the user's tolerance for latency and desired response vary dramatically based on use.

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    cover image ACM Conferences
    SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval
    July 2014
    1330 pages
    ISBN:9781450322577
    DOI:10.1145/2600428
    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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    Published: 03 July 2014

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    1. citation recommendation
    2. differential search.
    3. personalization

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    SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    Cited By

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    • (2023)Personalized literature recommendation based on heterogeneous entity academic networkJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10164935:8(101649)Online publication date: Sep-2023
    • (2022)SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.298214834:1(448-461)Online publication date: 1-Jan-2022
    • (2022)An empirical study of the design choices for local citation recommendation systemsExpert Systems with Applications10.1016/j.eswa.2022.116852200(116852)Online publication date: Aug-2022
    • (2022)Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-Based RerankingAdvances in Information Retrieval10.1007/978-3-030-99736-6_19(274-288)Online publication date: 10-Apr-2022
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    • (2021)EntityBot: Supporting Everyday Digital Tasks with Entity RecommendationsProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3478883(753-756)Online publication date: 13-Sep-2021
    • (2021)Entity Recommendation for Everyday Digital TasksACM Transactions on Computer-Human Interaction10.1145/345891928:5(1-41)Online publication date: 20-Aug-2021
    • (2021)A content-sensitive citation representation approach for citation recommendationJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-021-03153-513:6(3163-3174)Online publication date: 21-Mar-2021
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