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Boosting Local Recommendations With Partially Trained Global Model

Published: 13 September 2021 Publication History

Abstract

Building recommendation systems for enterprise software has many unique challenges that are different from consumer-facing systems. When applied to different organizations, the data used to power those recommendation systems vary substantially in both quality and quantity due to differences in their operational practices, marketing strategies, and targeted audiences. At Salesforce, as a cloud provider of such a system with data across many different organizations, naturally, it makes sense to pool data from different organizations to build a model that combines all values from different brands. However, multiple issues like how do we make sure a model trained with pooled data can still capture customer specific characteristics, how do we design the system to handle those data responsibly and ethically, i.e., respecting contractual agreements with our clients, legal and compliance requirements, and the privacy of all the consumers. In this proposal, We present a framework that not only utilizes enriched user-level data across organizations, but also boosts business-specific characteristics in generating personal recommendations. We will also walk through key privacy considerations when designing such a system.

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References

[1]
Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 1 (2004), 5–53. https://doi.org/10.1145/963770.963772
[2]
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (2009), 30–37. https://doi.org/10.1109/MC.2009.263

Cited By

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  • (2024)Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2024.336582812(28273-28296)Online publication date: 2024

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Published In

cover image ACM Conferences
RecSys '21: Proceedings of the 15th ACM Conference on Recommender Systems
September 2021
883 pages
ISBN:9781450384582
DOI:10.1145/3460231
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2021

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Author Tags

  1. global model
  2. privacy
  3. recommendation system

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RecSys '21: Fifteenth ACM Conference on Recommender Systems
September 27 - October 1, 2021
Amsterdam, Netherlands

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

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Cited By

View all
  • (2024)Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2024.336582812(28273-28296)Online publication date: 2024

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