Developing an Adaptive Feed Algorithm: A Case Study

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Developing an Adaptive Feed Algorithm: A Case Study

Type: Case Studies
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  • Volume : 1 Issue : 2 2023
  • Page Number : 19-21
  • Publication : ISRDO

Published Manuscript

Title

Developing an Adaptive Feed Algorithm: A Case Study

Author

1. Lucca Ferraz Vaz, Student, University of Sao Paulo, Brazil
2. Roberto Gropelli, Student, University of Brasília, Brazil

Abstract

This case study delves into the evolution of a feed algorithm for Company X, a social media platform aiming to enhance user engagement. The transition from a chronological feed to a personalized and dynamic algorithm involved a multifaceted approach. Key objectives included personalization, relevance, diversity, and real-time adaptability. The process involved data collection, content analysis, user behavior modeling, collaborative filtering, dynamic ranking, and A/B testing. Challenges, such as data privacy, algorithm bias, and scalability, were addressed through anonymization, diversity incorporation, and scalable infrastructure. The results demonstrated a substantial increase in user engagement, affirming the efficacy of the adaptive feed algorithm. This case study underscores the importance of a holistic and iterative approach to algorithm development for sustained user satisfaction in the digital content landscape.

Keywords

Feed Algorithm Personalization Relevance Data Collection Collaborative Filtering Dynamic Ranking User Engagement

Conclusion

Building an effective feed algorithm requires a holistic approach that considers user data, content analysis, and real-time user interactions. Continuous testing, iteration, and a commitment to addressing challenges ensure that the algorithm evolves with user needs and platform growth. The success of Company X demonstrates the importance of investing in algorithm development to enhance user experience in the dynamic landscape of digital content consumption.

Author Contrubution

The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.

Funding

The authors did not receive any specific grants from funding agencies in the public, commercial, or non-profit sectors for the research, authorship, and/or publication of this article.

Conflict of Interest

All authors declare that they have no conflicts of interest.

Data Sharing Statement

Not applicable

Software And Tools Use

Not applicable

Acknowledgements

I thank the following individuals for their expertise and assistance in all aspects of our study and for their help in writing the manuscript. I am also grateful for the insightful comments given by anonymous peer reviewers. Everyone's generosity and expertise have improved this study in myriad ways and saved me from many errors.

Corresponding Author

LV
Lucca Ferraz Vaz

University of Sao Paulo, Student, Brazil

RG
Roberto Gropelli

University of Brasília, Student, Brazil

Copyright

Copyright: ©2024 Corresponding Author. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Vaz, Lucca Ferraz, and Gropelli, Roberto. “Developing an Adaptive Feed Algorithm: A Case Study.” Scientific Research Journal of Science, Engineering and Technology, vol. 1, no. 2, 2023, pp. 19-21, https://isrdo.org/journal/SRJSET/currentissue/developing-an-adaptive-feed-algorithm-a-case-study

Vaz, L., & Gropelli, R. (2023). Developing an Adaptive Feed Algorithm: A Case Study. Scientific Research Journal of Science, Engineering and Technology, 1(2), 19-21. https://isrdo.org/journal/SRJSET/currentissue/developing-an-adaptive-feed-algorithm-a-case-study

Vaz Lucca Ferraz and Gropelli Roberto, Developing an Adaptive Feed Algorithm: A Case Study, Scientific Research Journal of Science, Engineering and Technology 1, no. 2(2023): 19-21, https://isrdo.org/journal/SRJSET/currentissue/developing-an-adaptive-feed-algorithm-a-case-study

829

Total words

436

Unique Words

48

Sentence

16.5

Avg Sentence Length

0.23265828500204

Subjectivity

0.059635416666667

Polarity

Text Statistics

  • Flesch Reading Ease : 21.5
  • Smog Index : 15.7
  • Flesch Kincaid Grade : 14.2
  • Coleman Liau Index : 18.5
  • Automated Readability Index : 17.4
  • Dale Chall Readability Score : 10.17
  • Difficult Words : 214
  • Linsear Write Formula : 17
  • Gunning Fog : 12.21
  • Text Standard : 16th and 17th grade

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