research-article

Movie Account Recommendation on Instagram

Authors:

Anthony J. T. LeeAuthors Info & Claims

ACM Transactions on Internet Technology, Volume 23, Issue 1

Article No.: 23, Pages 1 - 21

https://doi.org/10.1145/3579852

Published: 23 February 2023 Publication History

Abstract

With the increasing popularity of social networks, many businesses have started implementing their branding or targeted advertising strategies to reach potential customers through social media platforms. It is desirable and essential to help businesses to reach mass audiences and assist users to find favorite business accounts on social media platforms. In the movie industry, movie companies often create business accounts (movie accounts) to promote their movies and capture the attention of followers on Instagram. Instagram contains rich information about movies and user feedback, while IMDb, one of the most popular online databases, contains well-organized information related to movies. The features extracted from the data collected from Instagram and IMDb can complement each other. Therefore, in this study, we propose a framework for recommending movie accounts to users on Instagram by using the data collected from Instagram and IMDb platforms. The experiment results show that our proposed framework outperforms the comparing methods in terms of precision, recall, F1-score, and Normalized Discounted Cumulative Gain (NDCG), and mitigates the effect of cold start problems. The proposed framework can help movie companies or businesses reach potential audiences and implement effective targeted advertising strategies.

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Index Terms

Movie Account Recommendation on Instagram
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 23, Issue 1

February 2023

564 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3584863

Editor:
Ling Liu
Georgia Institute of Technology, USA

Issue’s Table of Contents

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 February 2023

Online AM: 13 January 2023

Accepted: 21 December 2022

Revised: 16 April 2022

Received: 24 April 2021

Published in TOIT Volume 23, Issue 1

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https://dl.acm.org/doi/10.1186/s13677-024-00645-5
Miao WXia YZhang RZhao XLi QWang TMeng S(2024)A secure data interaction method based on edge computingJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00617-913:1Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1186/s13677-024-00617-9
Zhu JHu CKhezri EGhazali M(2024)Edge intelligence-assisted animation design with large models: a surveyJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00601-313:1Online publication date: 21-Feb-2024
https://dl.acm.org/doi/10.1186/s13677-024-00601-3
Li DZhang HCheng JLiu B(2024)Improving efficiency of DNN-based relocalization module for autonomous driving with server-side computingJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00592-113:1Online publication date: 25-Jan-2024
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Sharma SShakya H(2023)Hybrid Real-Time Implicit Feedback SOM-Based Movie Recommendation SystemsProceedings of Fourth International Conference on Computing, Communications, and Cyber-Security10.1007/978-981-99-1479-1_28(371-388)Online publication date: 2-Jul-2023
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