research-article

Personalized Re-ranking with Item Relationships for E-commerce

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Pheng Ann HengAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 925 - 934

https://doi.org/10.1145/3340531.3412332

Published: 19 October 2020 Publication History

Abstract

Re-ranking is a critical task for large-scale commercial recommender systems. Given the initial ranked lists, top candidates are re-ranked to improve the accuracy of the ranking results. However, existing re-ranking strategies are sub-optimal due to (i) most prior works do not consider explicit item relationships, like being substitutable or complementary, which may mutually influence the user satisfaction on other items in the lists, and (ii) they usually apply an identical re-ranking strategy for all users, with personalized user preferences and intents ignored. To resolve the problem, we construct a heterogeneous graph to fuse the initial scoring information and item relationships information. We develop a graph neural network based framework, IRGPR, to explicitly model transitive item relationships by recursively aggregating relational information from multi-hop neighborhoods. We also incorporate a novel intent embedding network to embed personalized user intents into the propagation. We conduct extensive experiments on real-world datasets, demonstrating the effectiveness of IRGPR in re-ranking. Further analysis reveals that modeling the item relationships and personalized intents are particularly useful for improving the performance of re-ranking.

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Xi YLiu WDai XTang RLiu QZhang WYu Y(2024)Utility-Oriented Reranking with Counterfactual ContextACM Transactions on Knowledge Discovery from Data10.1145/367100418:8(1-22)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3671004
Xu SPang LXu JShen HCheng XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented GenerationProceedings of the ACM Web Conference 202410.1145/3589334.3645336(1330-1340)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645336
Chu XHao CWang SYin DZhao JZou LLi C(2024) LT 2 R: Learning to Online Learning to Rank for Web Search 2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00360(4733-4746)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00360
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Index Terms

Personalized Re-ranking with Item Relationships for E-commerce
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

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Published: 19 October 2020

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Cited By

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https://dl.acm.org/doi/10.1145/3671004
Xu SPang LXu JShen HCheng XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented GenerationProceedings of the ACM Web Conference 202410.1145/3589334.3645336(1330-1340)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645336
Chu XHao CWang SYin DZhao JZou LLi C(2024) LT 2 R: Learning to Online Learning to Rank for Web Search 2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00360(4733-4746)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00360
Xu WGuo YLi WLin JLi S(2024)GC-UNet: Enhance UNet with GCN for Periodontitis Segmentation2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10821919(2740-2747)Online publication date: 3-Dec-2024
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Chen XZhu RDing BWang SZhou J(2024)Lero: applying learning-to-rank in query optimizerThe VLDB Journal10.1007/s00778-024-00850-333:5(1307-1331)Online publication date: 25-Apr-2024
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Liu HLi LYu NMa KPeng THu X(2024)Outfit compatibility model using fully connected self-adjusting graph neural networkThe Visual Computer10.1007/s00371-023-03238-640:11(8331-8343)Online publication date: 22-Jan-2024
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Choudhary TAggarwal VJain PTrivedi S(2023)A MultiCriteria Approach in ECommerce Product RankingProceedings of the 5th International Conference on Information Management & Machine Intelligence10.1145/3647444.3647891(1-6)Online publication date: 23-Nov-2023
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Yang SZhang LXia FLin LFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Graph Exploration Matters: Improving both Individual-Level and System-Level Diversity in WeChat Feed RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614688(4901-4908)Online publication date: 21-Oct-2023
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Chen JWang JDai ZWu HWang MZhang QWang HEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Zero-shot Micro-video Classification with Neural Variational Inference in Graph Prototype NetworkProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611740(966-974)Online publication date: 26-Oct-2023
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Xi YLiu WWang YTang RZhang WZhu YZhang RYu YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)On-device Integrated Re-ranking with Heterogeneous Behavior ModelingProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599878(5225-5236)Online publication date: 6-Aug-2023
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