When actions speak louder than clicks: A combined model of purchase probability and long-term customer satisfaction

Gal Lavee, Noam Koenigstein, Oren Barkan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Maximizing sales and revenue is an important goal of online commercial retailers. Recommender systems are designed to maximize users' click or purchase probability, but often disregard users' eventual satisfaction with purchased items. As result, such systems promote items with high appeal at the selling stage (e.g. an eye-catching presentation) over items that would yield more satisfaction to users in the long run. This work presents a novel unified model that considers both goals and can be tuned to balance between them according to the needs of the business scenario. We propose a multi-task probabilistic matrix factorization model with a dual task objective: predicting binary purchase/no purchase variables combined with predicting continuous satisfaction scores. Model parameters are optimized using Variational Bayes which allows learning a posterior distribution over model parameters. This model allows making predictions that balance the two goals of maximizing the probability for an immediate purchase and maximizing user satisfaction and engagement down the line. These goals lie at the heart of most commercial recommendation scenarios and enabling their balance has the potential to improve value for millions of users worldwide. Finally, we present experimental evaluation on different types of consumer retail datasets that demonstrate the benefits of the model over popular baselines on a number of well-known ranking metrics.

Original languageEnglish
Title of host publicationRecSys 2019 - 13th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages287-295
Number of pages9
ISBN (Electronic)9781450362436
DOIs
StatePublished - 10 Sep 2019
Event13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark
Duration: 16 Sep 201920 Sep 2019

Publication series

NameRecSys 2019 - 13th ACM Conference on Recommender Systems

Conference

Conference13th ACM Conference on Recommender Systems, RecSys 2019
Country/TerritoryDenmark
CityCopenhagen
Period16/09/1920/09/19

Keywords

  • Continuous Implicit Data
  • Recommendations
  • Variational Methods

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