short-paper

An Industrial Framework for Cold-Start Recommendation in Zero-Shot Scenarios

Authors:

Linjian MoAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 3403 - 3407

https://doi.org/10.1145/3477495.3536332

Published: 07 July 2022 Publication History

Abstract

There exists the cold-start problem in the recommendation systems when observed user-item interactions are insufficient. To alleviate this problem, most existing works aim to learn globally shared prior knowledge across all items and be fast adapted to a new item with few interactions. However, such learning techniques are data demanding and work poorly on new items with no interactions. In this applied paper, we present an industrial framework recently deployed on Alipay to address the item cold-start problem in zero-shot scenarios. The proposed framework provides both efficient and high-quality recommendations for cold items with no log data. Specifically, we formulate the cold-start problem as a zero-shot learning problem and build a highly efficient infrastructure to accomplish online zero-shot recommendations used on large-scale platforms. Extensive offline experiments and online A/B testing demonstrate that the proposed framework has superior performance and recommends cold items to preferred users more effectively than other state-of-the-art methods.

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Cited By

Fu ZNiu XMaher M(2023)Deep Learning Models for Serendipity Recommendations: A Survey and New PerspectivesACM Computing Surveys10.1145/360514556:1(1-26)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3605145
Xiao YZhao SZhou ZHuan ZJu LZhang XWang LZhou JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615208(4365-4369)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615208
Fan ZLiu ZHeinecke SZhang JWang HXiong CYu PFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-TrainingProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615110(483-493)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615110
Show More Cited By

Index Terms

An Industrial Framework for Cold-Start Recommendation in Zero-Shot Scenarios
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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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SIGIR: ACM Special Interest Group on Information Retrieval

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Publication History

Published: 07 July 2022

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SIGIR '22

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SIGIR

SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Fu ZNiu XMaher M(2023)Deep Learning Models for Serendipity Recommendations: A Survey and New PerspectivesACM Computing Surveys10.1145/360514556:1(1-26)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3605145
Xiao YZhao SZhou ZHuan ZJu LZhang XWang LZhou JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615208(4365-4369)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615208
Fan ZLiu ZHeinecke SZhang JWang HXiong CYu PFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-TrainingProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615110(483-493)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615110
Wang WLin XWang LFeng FWei YChua TEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Equivariant Learning for Out-of-Distribution Cold-start RecommendationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612522(903-914)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612522
Zhang YZhou JRen YZhang YYang XLi MShi QLi L(2023)ALT: An Automatic System for Long Tail Scenario Modeling2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00231(3017-3030)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00231

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