skip to main content
10.1145/3386392.3397594acmconferencesArticle/Chapter ViewAbstractPublication PagesumapConference Proceedingsconference-collections
research-article

Keeping up with the Influencers: Improving User Recommendation in Instagram using Visual Content

Published:13 July 2020Publication History

ABSTRACT

In the social media domain user-to-user recommendation is an important factor to suggest new content and to strengthen the user social circle. In this paper we investigate how to improve user-to-user recommendation exploiting a user similarity metric computed analysing the photos shared by users on their Instagram profile. We consider in particular users with an established credibility and audience, the so called "influencers". The main idea is that if two influencers publish photos containing similar content it is more likely that they share the same interests and are similar. Moreover, users that follow other users sharing related content are also more similar. Similarity between influencers' photo collections is estimated through neural network embeddings, using a network trained to classify photo collections in categories of interest. An hybrid recommendation approach, which combines collaborative filtering and results from this compact representation of visual content of photo collections, is proposed. Experiments on a large dataset of ~4.8M Instagram users show how our visual approach enhances the performance of a user-to-user recommender with respect to a baseline recommendation algorithm based on collaborative filtering.

Skip Supplemental Material Section

Supplemental Material

3386392.3397594.mp4

References

  1. Saeideh Bakhshi, David A. Shamma, and Eric Gilbert. 2014. Faces Engage Us: Photos with Faces Attract More Likes and Comments on Instagram. In Proc. of SIGCHI Conference on Human Factors in Computing Systems (CHI). 965--974.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. Cheung, J. She, and Z. Jie. 2015. Connection Discovery Using Big Data of User-Shared Images in Social Media. IEEE Transactions on Multimedia, Vol. 17, 9 (Sep. 2015), 1417--1428.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Ryadh Dahimene, Camelia Constantin, and Cédric du Mouza. 2014. RecLand: A Recommender System for Social Networks. In Proc. of CIKM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Marijke De Veirman, Veroline Cauberghe, and Liselot Hudders. 2017. Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude. International Journal of Advertising, Vol. 36, 5 (2017), 798--828.Google ScholarGoogle ScholarCross RefCross Ref
  5. Xuetao Ding, Xiaoming Jin, Yujia Li, and Lianghao Li. 2013. Celebrity recommendation with collaborative social topic regression. In Proc. of IJCAI.Google ScholarGoogle Scholar
  6. C. Fan, H. Hao, C. K. Leung, L. Y. Sun, and J. Tran. 2018. Social Network Mining for Recommendation of Friends Based on Music Interests. In Proc. of ASONAM.Google ScholarGoogle Scholar
  7. Bruce Ferwerda and Marko Tkalcic. 2018. Predicting Users' Personality from Instagram Pictures: Using Visual and/or Content Features?. In Proc. of UMAP. 5.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Francesco Gelli, Xiangnan He, Tao Chen, and Tat-Seng Chua. 2017. How personality affects our likes: Towards a better understanding of actionable images. In Proc. of ACM Multimedia (MM).Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Dongyan Guo, Jingsong Xu, Jian Zhang, Min Xu, Ying Cui, and Xiangjian He. 2017. User relationship strength modeling for friend recommendation on Instagram. Neurocomputing, Vol. 239 (2017), 9 -- 18.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Ido Guy. 2018. People Recommendation on Social Media .Springer International Publishing, Cham, 570--623.Google ScholarGoogle Scholar
  11. Alexander Hermans, Lucas Beyer, and Bastian Leibe. 2017. In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017).Google ScholarGoogle Scholar
  12. Susie Khamis, Lawrence Ang, and Raymond Welling. 2017. Self-branding, 'micro-celebrity' and the rise of Social Media Influencers. Celebrity Studies, Vol. 8, 2 (2017), 191--208.Google ScholarGoogle ScholarCross RefCross Ref
  13. Joseph A Konstan, Bradley N Miller, David Maltz, Jonathan L Herlocker, Lee R Gordon, and John Riedl. 1997. GroupLens: applying collaborative filtering to Usenet news. Commun. ACM, Vol. 40, 3 (1997), 77--87.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Maciej Kula. 2015. Metadata embeddings for user and item cold-start recommendations. In Proc. of CBRecSys.Google ScholarGoogle Scholar
  15. Bamshad Mobasher. 2007. Data mining for web personalization. The adaptive web (2007).Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proc. of UAI.Google ScholarGoogle Scholar
  17. D. Semedo, J. Magalh aes, and F. Martins. 2018. Inferring User Gender from User Generated Visual Content on a Deep Semantic Space. In Proc. of EUSIPCO.Google ScholarGoogle Scholar
  18. Junho Song, Kyungsik Han, Dongwon Lee, and Sang-Wook Kim. 2018. “Is a picture really worth a thousand words?”: A case study on classifying user attributes on Instagram. PLOS ONE, Vol. 13, 10 (10 2018), 1--22.Google ScholarGoogle ScholarCross RefCross Ref
  19. Z. Wang, J. Liao, Q. Cao, H. Qi, and Z. Wang. 2015. Friendbook: A Semantic-Based Friend Recommendation System for Social Networks. IEEE Transactions on Mobile Computing, Vol. 14, 3 (March 2015), 538--551.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Jian Wei, Jianhua He, Kai Chen, Yi Zhou, and Zuoyin Tang. 2017. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, Vol. 69 (2017), 29 -- 39.Google ScholarGoogle ScholarCross RefCross Ref
  21. Jason Weston, Samy Bengio, and Nicolas Usunier. 2011. Wsabie: Scaling up to large vocabulary image annotation. In Proc. of IJCAI.Google ScholarGoogle Scholar

Index Terms

  1. Keeping up with the Influencers: Improving User Recommendation in Instagram using Visual Content

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Conferences
              UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization
              July 2020
              395 pages
              ISBN:9781450379502
              DOI:10.1145/3386392

              Copyright © 2020 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]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 13 July 2020

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article

              Acceptance Rates

              Overall Acceptance Rate162of633submissions,26%

              Upcoming Conference

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader