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I pinned it. where can i buy one like it?: automatically linking pinterest pins to online webshops

Published: 28 October 2013 Publication History

Abstract

The information that users of social network sites post often points towards their interests and hobbies. It can be used to recommend relevant products to users. In this paper we implement and evaluate several information retrieval models for linking the texts of pins of Pinterest to webpages of Amazon, and ranking the pages (which we call webshops) according to the personal interest of the pinner. The results show that models that combine latent concepts composed of related terms with single words yield the best performance.

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Cited By

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  • (2018)Extending Knowledge Graphs with Subjective Influence Networks for Personalized FashionDesigning Cognitive Cities10.1007/978-3-030-00317-3_9(203-233)Online publication date: 19-Sep-2018
  • (2015)Probabilistic topic modeling in multilingual settings: An overview of its methodology and applicationsInformation Processing & Management10.1016/j.ipm.2014.08.00351:1(111-147)Online publication date: Jan-2015
  • (2014)Large-scale cross-media analysis and mining from socially curated contentsProgress in Informatics10.2201/NiiPi.2014.11.4(19)Online publication date: Mar-2014
  • Show More Cited By

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  1. I pinned it. where can i buy one like it?: automatically linking pinterest pins to online webshops

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      cover image ACM Conferences
      DUBMOD '13: Proceedings of the 2013 workshop on Data-driven user behavioral modelling and mining from social media
      October 2013
      40 pages
      ISBN:9781450324175
      DOI:10.1145/2513577
      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: 28 October 2013

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      Author Tags

      1. personalized linking
      2. recommendation systems
      3. topic models
      4. user interest

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      DUBMOD '13 Paper Acceptance Rate 8 of 12 submissions, 67%;
      Overall Acceptance Rate 15 of 20 submissions, 75%

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      Cited By

      View all
      • (2018)Extending Knowledge Graphs with Subjective Influence Networks for Personalized FashionDesigning Cognitive Cities10.1007/978-3-030-00317-3_9(203-233)Online publication date: 19-Sep-2018
      • (2015)Probabilistic topic modeling in multilingual settings: An overview of its methodology and applicationsInformation Processing & Management10.1016/j.ipm.2014.08.00351:1(111-147)Online publication date: Jan-2015
      • (2014)Large-scale cross-media analysis and mining from socially curated contentsProgress in Informatics10.2201/NiiPi.2014.11.4(19)Online publication date: Mar-2014
      • (2014)Collecting, organizing, and sharing pins in pinterestACM SIGMETRICS Performance Evaluation Review10.1145/2637364.259199642:1(15-27)Online publication date: 16-Jun-2014
      • (2014)Collecting, organizing, and sharing pins in pinterestThe 2014 ACM international conference on Measurement and modeling of computer systems10.1145/2591971.2591996(15-27)Online publication date: 16-Jun-2014
      • (2014)Multilingual probabilistic topic modeling and its applications in web mining and searchProceedings of the 7th ACM international conference on Web search and data mining10.1145/2556195.2556200(681-682)Online publication date: 24-Feb-2014

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