research-article

Spotting trends: the wisdom of the few

Authors:

Daniele Quercia,

Pietro Michiardi,

Matteo Dell'AmicoAuthors Info & Claims

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

Pages 51 - 58

https://doi.org/10.1145/2365952.2365967

Published: 09 September 2012 Publication History

Abstract

Social media sites have used recommender systems to suggest items users might like but are not already familiar with. These items are typically movies, books, pictures, or songs. Here we consider an alternative class of items - pictures posted by design-conscious individuals. We do so in the context of a mobile application in which users find "cool" items in the real world, take pictures of them, and share those pictures online. In this context, temporal dynamics matter, and users would greatly profit from ways of identifying the latest design trends. We propose a new way of recommending trending pictures to users, which unfolds in three steps. First, two types of users are identified - those who are good at uploading trends (trend makers) and those who are experienced in discovering trends (trend spotters). Second, based on what those "special few" have uploaded and rated, trends are identified early on. Third, trends are recommended using existing algorithms. Upon the complete longitudinal dataset of the mobile application, we compare our approach's performance to a traditional recommender system's.

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Cited By

Tkalcic MChen L(2015)Personality and Recommender SystemsRecommender Systems Handbook10.1007/978-1-4899-7637-6_21(715-739)Online publication date: 2015
https://doi.org/10.1007/978-1-4899-7637-6_21
Shoji YKato MTanaka K(2014)Can Diversity Improve Credibility of User Review Data?Social Informatics10.1007/978-3-319-13734-6_17(244-258)Online publication date: 2014
https://doi.org/10.1007/978-3-319-13734-6_17
Amatriain X(2013)Mining large streams of user data for personalized recommendationsACM SIGKDD Explorations Newsletter10.1145/2481244.248125014:2(37-48)Online publication date: 30-Apr-2013
https://dl.acm.org/doi/10.1145/2481244.2481250
Show More Cited By

Index Terms

Spotting trends: the wisdom of the few
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

September 2012

376 pages

ISBN:9781450312707

DOI:10.1145/2365952

General Chairs:
Pádraig Cunningham
University College Dublin, Ireland
,
Neil Hurley
University College Dublin, Ireland
,
Program Chairs:
Ido Guy
IBM Haifa Research Laboratory, Israel
,
Sarabjot Singh Anand
University of Warwick, England

Copyright © 2012 ACM.

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: 09 September 2012

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RecSys '12

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SIGCHI

RecSys '12: Sixth ACM Conference on Recommender Systems

September 9 - 13, 2012

Dublin, Ireland

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RecSys '12 Paper Acceptance Rate 24 of 119 submissions, 20%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Tkalcic MChen L(2015)Personality and Recommender SystemsRecommender Systems Handbook10.1007/978-1-4899-7637-6_21(715-739)Online publication date: 2015
https://doi.org/10.1007/978-1-4899-7637-6_21
Shoji YKato MTanaka K(2014)Can Diversity Improve Credibility of User Review Data?Social Informatics10.1007/978-3-319-13734-6_17(244-258)Online publication date: 2014
https://doi.org/10.1007/978-3-319-13734-6_17
Amatriain X(2013)Mining large streams of user data for personalized recommendationsACM SIGKDD Explorations Newsletter10.1145/2481244.248125014:2(37-48)Online publication date: 30-Apr-2013
https://dl.acm.org/doi/10.1145/2481244.2481250
Tkalčič MChen L(2012)Personality and Recommender SystemsRecommender Systems Handbook10.1007/978-1-0716-2197-4_20(757-787)Online publication date: 24-Feb-2012
https://doi.org/10.1007/978-1-0716-2197-4_20

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