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Personalized recommendation driven by information flow

Published: 06 August 2006 Publication History

Abstract

We propose that the information access behavior of a group of people can be modeled as an information flow issue, in which people intentionally or unintentionally influence and inspire each other, thus creating an interest in retrieving or getting a specific kind of information or product. Information flow models how information is propagated in a social network. It can be a real social network where interactions between people reside; it can be, moreover, a virtual social network in that people only influence each other unintentionally, for instance, through collaborative filtering. We leverage users' access patterns to model information flow and generate effective personalized recommendations. First, an early adoption based information flow (EABIF) network describes the influential relationships between people. Second, based on the fact that adoption is typically category specific, we propose a topic-sensitive EABIF (TEABIF) network, in which access patterns are clustered with respect to the categories. Once an item has been accessed by early adopters, personalized recommendations are achieved by estimating whom the information will be propagated to with high probabilities. In our experiments with an online document recommendation system, the results demonstrate that the EABIF and the TEABIF can respectively achieve an improved (precision, recall) of (91.0%, 87.1%) and (108.5%, 112.8%) compared to traditional collaborative filtering, given an early adopter exists.

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cover image ACM Conferences
SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
August 2006
768 pages
ISBN:1595933697
DOI:10.1145/1148170
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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Publication History

Published: 06 August 2006

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Author Tags

  1. collaborative filtering
  2. information flow
  3. personalized recommendation

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SIGIR06
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SIGIR06: The 29th Annual International SIGIR Conference
August 6 - 11, 2006
Washington, Seattle, USA

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

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  • (2024)Complementary influence maximization under comparative linear threshold modelExpert Systems with Applications10.1016/j.eswa.2023.121826238(121826)Online publication date: Mar-2024
  • (2024)The Impact of Health Opportunities on Social MoodDigital Geography10.1007/978-3-031-67762-5_27(355-362)Online publication date: 9-Nov-2024
  • (2023)Attention-Based Time Sequence and Distance Contexts Gated Recurrent Unit for Personalized POI RecommendationInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.32579016:2(1-14)Online publication date: 10-Jul-2023
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