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Variational learning to rank (VL2R)

Published: 27 September 2018 Publication History

Abstract

We present Variational Learning to Rank (VL2R), a combination of variational inference and learning to rank. The combination provides a natural way to balance exploration and exploitation of the algorithm by introducing shuffling of product search/category listings according to the model's relevance uncertainty for each product. Simply put, we perturb (newer) products with higher uncertainty on the relevance more than (older) products which have a lower uncertainty on the relevance.
Our formalism makes it possible to train an end-to-end model that optimizes for both ranking and shuffling, compared to known state-of-the-art systems where ranking and shuffling are treated as separate problems. VL2R provides an integrated way of doing propensity scoring during the offline learning phase, thus reducing selection bias. The system is simple, yet powerful and flexible. We have implemented it within the Salesforce Commerce Cloud; a platform 500 million unique online shoppers interact with each month across 2,750 websites in 53+ countries as of FY18.
In this talk, we will go into the details of our variational learning to rank system and share our early experiences with optimizing VL2R and running it in production. We hope that by sharing VL2R with the recommendation systems community, we will foster more research in this direction, and result in systems that are faster at learning user preferences for changing catalogs.

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MP4 File (p480-lundergaard.mp4)

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cover image ACM Conferences
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems
September 2018
600 pages
ISBN:9781450359016
DOI:10.1145/3240323
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: 27 September 2018

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Author Tags

  1. propensity scoring
  2. recommender systems
  3. salesforce
  4. variational autoencoders
  5. variational inference

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  • Invited-talk

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RecSys '18
Sponsor:
RecSys '18: Twelfth ACM Conference on Recommender Systems
October 2, 2018
British Columbia, Vancouver, Canada

Acceptance Rates

RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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