Session 17: Interactive Recommendation 2

Date: Friday September 22, 4:05 PM – 5:25 PM (GMT+8)
Room: Hall 406CX
Session Chair: Ludovico Boratto
Parallel with: Session 18: Women In RecSys

• INDContextual Multi-Armed Bandit for Email Layout Recommendation
  by Yan Chen (Wayfair), Emilian Vankov (Wayfair), Linas Baltrunas (Netflix), Preston Donovan (Wayfair), Akash Mehta (Wayfair) and Benjamin Schroeder (Wayfair).

  We present the use of a contextual multi-armed bandit approach to improve the personalization of marketing emails sent to Wayfair’s customers. Emails are a critical outreach tool as they economically unlock a significant amount of revenue. We describe how we formulated our problem of selecting the optimal personalized email layout to use as a contextual multi-armed bandit problem. We also explain how we approximated a solution with an Epsilon-greedy strategy. We detail the thorough evaluations we ran, including offline experiments, an off-policy evaluation, and an online A/B test. Our results demonstrate that our approach is able to select personalized email layouts that lead to significant gains in topline business metrics including engagement and conversion rates.

  Full text in ACM Digital Library

• INDReward innovation for long-term member satisfaction
  by Gary Tang (Netflix), Jiangwei Pan (Netflix), Henry Wang (Netflix) and Justin Basilico (Netflix).

  Many large-scale recommender systems train on engagements because of their data abundance, immediacy of feedback, and correlation to user preferences. At Netflix and many digital products, engagement is an imperfect proxy to the overall goal of long-term user satisfaction. One way we address this misalignment is via reward innovation. In this paper, we provide a high-level description of the problem and motivate our approach. Finally, we present some practical insights into this track of work including challenges, lessons learned, and systems we’ve built to support the effort.

  Full text in ACM Digital Library

• RESIncentivizing Exploration in Linear Contextual Bandits under Information Gap
  by Huazheng Wang (Oregon State University), Haifeng Xu (University of Chicago), Chuanhao Li (University of Virginia), Zhiyuan Liu (University of colorado,boulder) and Hongning Wang (University of Virginia).

  Contextual bandit algorithms have been popularly used to address interactive recommendation, where the users are assumed to be cooperative to explore all recommendations from a system. In this paper, we relax this strong assumption and study the problem of incentivized exploration with myopic users, where the users are only interested in recommendations with their currently highest estimated reward. As a result, in order to obtain long-term optimality, the system needs to offer compensation to incentivize the users to take the exploratory recommendations. We consider a new and practically motivated setting where the context features employed by the user are more \emph{informative} than those used by the system: for example, features based on users’ private information are not accessible by the system. We develop an effective solution for incentivized exploration under such an information gap, and prove that the method achieves a sublinear rate in both regret and compensation. We theoretically and empirically analyze the added compensation due to the information gap, compared with the case where the system has access to the same context features as the user does, i.e., without information gap. Moreover, we also provide a compensation lower bound of this problem.

  Full text in ACM Digital Library

• INDAdaptEx: a self-service contextual bandit platform
  by William Black (Expedia Group), Ercument Ilhan (Expedia Group), Andrea Marchini (Expedia Group) and Vilda Markeviciute (Expedia Group).

  This paper presents AdaptEx, a self-service contextual bandit platform widely used at Expedia Group, that leverages multi-armed bandit algorithms to personalize user experiences at scale. AdaptEx considers the unique context of each visitor to select the optimal variants and learns quickly from every interaction they make. It offers a powerful solution to improve user experiences while minimizing the costs and time associated with traditional testing methods. The platform unlocks the ability to iterate towards optimal product solutions quickly, even in ever-changing content and continuous “cold start” situations gracefully.

  Full text in ACM Digital Library

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