research-article

Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale

Authors:

Barrie Kersbergen,

Olivier Sprangers,

Sebastian SchelterAuthors Info & Claims

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

Pages 150 - 159

https://doi.org/10.1145/3514221.3517901

Published: 11 June 2022 Publication History

Abstract

Session-based recommendation predicts the next item with which a user will interact, given a sequence of her past interactions with other items. This machine learning problem targets a core scenario in e-commerce platforms, which aim to recommend interesting items to buy to users browsing the site. Session-based recommenders are difficult to scale due to their exponentially large input space of potential sessions. This impedes offline precomputation of the recommendations, and implies the necessity to maintain state during the online computation of next-item recommendations.

We propose VMIS-kNN, an adaptation of a state-of-the-art nearest neighbor approach to session-based recommendation, which leverages a prebuilt index to compute next-item recommendations with low latency in scenarios with hundreds of millions of clicks to search through. Based on this approach, we design and implement the scalable session-based recommender system Serenade, which is in production usage at bol.com, a large European e-commerce platform.

We evaluate the predictive performance of VMIS-kNN, and show that Serenade can answer a thousand recommendation requests per second with a 90th percentile latency of less than seven milliseconds in scenarios with millions of items to recommend. Furthermore, we present results from a three week long online A/B test with up to 600 requests per second for 6.5 million distinct items on more than 45 million user sessions from our e-commerce platform. To the best of our knowledge, we provide the first empirical evidence that the superior predictive performance of nearest neighbor approaches to session-based recommendation in offline evaluations translates to superior performance in a real world e-commerce setting.

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Cited By

Sirin UIdreos S(2024)The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage FormatProceedings of the ACM on Management of Data10.1145/36393072:1(1-31)Online publication date: 26-Mar-2024
https://doi.org/10.1145/3639307
Zhang LShen DKou YNie T(2024)Multi-perspective learning for enhanced user preferences for session-based recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111997298(111997)Online publication date: Aug-2024
https://doi.org/10.1016/j.knosys.2024.111997
Ezeife CKarlapalepu H(2023)A Survey of Sequential Pattern Based E-Commerce Recommendation SystemsAlgorithms10.3390/a1610046716:10(467)Online publication date: 3-Oct-2023
https://doi.org/10.3390/a16100467
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Index Terms

Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale
1. Information systems
  1. Data management systems

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cover image ACM Conferences

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

June 2022

2597 pages

ISBN:9781450392495

DOI:10.1145/3514221

General Chair:
Zachary Ives
University of Pennsylvania (USA)
,
Program Chairs:
Angela Bonifati
Lyon 1 University (France)
,
Amr El Abbadi
University of California, Santa Barbara (USA)

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 11 June 2022

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Ahold Delhaize

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SIGMOD/PODS '22: International Conference on Management of Data

June 12 - 17, 2022

PA, Philadelphia, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Sirin UIdreos S(2024)The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage FormatProceedings of the ACM on Management of Data10.1145/36393072:1(1-31)Online publication date: 26-Mar-2024
https://doi.org/10.1145/3639307
Zhang LShen DKou YNie T(2024)Multi-perspective learning for enhanced user preferences for session-based recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111997298(111997)Online publication date: Aug-2024
https://doi.org/10.1016/j.knosys.2024.111997
Ezeife CKarlapalepu H(2023)A Survey of Sequential Pattern Based E-Commerce Recommendation SystemsAlgorithms10.3390/a1610046716:10(467)Online publication date: 3-Oct-2023
https://doi.org/10.3390/a16100467
Schelter SAriannezhad Mde Rijke MChen HDuh WHuang HKato MMothe JPoblete B(2023)Forget Me Now: Fast and Exact Unlearning in Neighborhood-based RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591989(2011-2015)Online publication date: 18-Jul-2023
https://doi.org/10.1145/3539618.3591989
Ariannezhad MLi MSchelter Sde Rijke MChua TLauw HSi LTerzi ETsaparas P(2023)A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery ShoppingProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570417(87-95)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570417
Xie XNiu JDeng LWang DZhang JWu ZBian KCao GCui B(2023)Hierarchical Interest Modeling of Long-tailed Users for Click-Through Rate Prediction2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00234(3058-3071)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00234

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