research-article

Tensorized Determinantal Point Processes for Recommendation

Authors:

Jérémie Mary,

Mike GartrellAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 1605 - 1615

https://doi.org/10.1145/3292500.3330952

Published: 25 July 2019 Publication History

Abstract

Interest in determinantal point processes (DPPs) is increasing in machine learning due to their ability to provide an elegant parametric model over combinatorial sets. In particular, the number of required parameters in a DPP grows only quadratically with the size of the ground set (e.g., item catalog), while the number of possible sets of items grows exponentially. Recent work has shown that DPPs can be effective models for product recommendation and basket completion tasks, since they are able to account for both the diversity and quality of items within a set. We present an enhanced DPP model that is specialized for the task of basket completion, the tensorized DPP. We leverage ideas from tensor factorization in order to customize the model for the next-item basket completion task, where the next item is captured in an extra dimension of the model. We evaluate our model on several real-world datasets, and find that the tensorized DPP provides significantly better predictive quality in several settings than a number of state-of-the art models.

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Cited By

Shi CRen PFu DXin XYang SCai FRen ZChen Z(2024)Diversifying Sequential Recommendation with Retrospective and Prospective TransformersACM Transactions on Information Systems10.1145/365301642:5(1-37)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3653016
Pandey SDas SGanu HSingh SBalsamo SKnottenbelt WAbad CShang W(2024)Rethinking 'Complement' Recommendations at Scale with SIMDProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645041(25-36)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629526.3645041
Sukiennik NGao CLi NChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3648159(4727-4735)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3648159
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Index Terms

Tensorized Determinantal Point Processes for Recommendation
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Electronic commerce
        Online shopping
    2. Web searching and information discovery

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cover image ACM Conferences

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
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University of Minnesota
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Ying Li
EV Analysis Corporation
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LinkedIn
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Boston University
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University of Minnesota

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Published: 25 July 2019

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KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Shi CRen PFu DXin XYang SCai FRen ZChen Z(2024)Diversifying Sequential Recommendation with Retrospective and Prospective TransformersACM Transactions on Information Systems10.1145/365301642:5(1-37)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3653016
Pandey SDas SGanu HSingh SBalsamo SKnottenbelt WAbad CShang W(2024)Rethinking 'Complement' Recommendations at Scale with SIMDProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645041(25-36)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629526.3645041
Sukiennik NGao CLi NChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3648159(4727-4735)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3648159
Li FLi HDong R(2024)PRDG: Personalized Recommendation with Diversity Based on Graph Neural Networks2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651479(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651479
Liu YWalder CXie L(2024)Learning k-Determinantal Point Processes for Personalized Ranking2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00084(1036-1049)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00084
Zuo YLiu SZhou Y(2023)DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative FilteringApplied Sciences10.3390/app1305294513:5(2945)Online publication date: 24-Feb-2023
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Ohsaka NTogashi RChen HDuh WHuang HKato MMothe JPoblete B(2023)Curse of "Low" Dimensionality in Recommender SystemsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591659(537-547)Online publication date: 19-Jul-2023
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Zhang XLi CShi HZhu XLi PDong J(2023)AdapNet: Adaptability Decomposing Encoder–Decoder Network for Weakly Supervised Action Recognition and LocalizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2019.296281534:4(1852-1863)Online publication date: Apr-2023
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