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Prediction of Sparse User-Item Consumption Rates with Zero-Inflated Poisson Regression

Authors:

Padhraic SmythAuthors Info & Claims

WWW '18: Proceedings of the 2018 World Wide Web Conference

Pages 719 - 728

https://doi.org/10.1145/3178876.3186153

Published: 23 April 2018 Publication History

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Abstract

In this paper we address the problem of building user models that can predict the rate at which individuals consume items from a finite set, including items they have consumed in the past and items that are new. This combination of repeat and new item consumption is common in applications such as listening to music, visiting web sites, and purchasing products. We use zero-inflated Poisson (ZIP) regression models as the basis for our modeling approach, leading to a general framework for modeling user-item consumption rates over time. We show that these models are more flexible in capturing user behavior than alternatives such as well-known latent factor models based on matrix factorization. We compare the performance of ZIP regression and latent factor models on three different data sets involving music, restaurant reviews, and social media. The ZIP regression models are systematically more accurate across all three data sets and across different prediction metrics.

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Cited By

Lisnic MLex AKogan M(2024)"Yeah, this graph doesn't show that": Analysis of Online Engagement with Misleading Data VisualizationsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642448(1-14)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642448
Wang ZWei WFeng SMao XQiu MChen DFang R(2024)Exploiting Group-Level Behavior Pattern for Session-Based RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328031036:1(152-166)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3280310
Ma YGan M(2024)Sequential-hierarchical attention network: Exploring the hierarchical intention feature in POI recommendationWorld Wide Web10.1007/s11280-024-01295-y27:6Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s11280-024-01295-y
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Index Terms

Prediction of Sparse User-Item Consumption Rates with Zero-Inflated Poisson Regression
1. Information systems
  1. Information systems applications
    1. Data mining
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '18: Proceedings of the 2018 World Wide Web Conference

April 2018

2000 pages

ISBN:9781450356398

General Chairs:
Pierre-Antoine Champin
Universitè Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

Copyright © 2018 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 ACM 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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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 23 April 2018

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Funding Sources

Google Faculty Award
National Science Foundation

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WWW '18

Sponsor:

IW3C2

WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

Acceptance Rates

WWW '18 Paper Acceptance Rate 170 of 1,155 submissions, 15%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Lisnic MLex AKogan M(2024)"Yeah, this graph doesn't show that": Analysis of Online Engagement with Misleading Data VisualizationsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642448(1-14)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642448
Wang ZWei WFeng SMao XQiu MChen DFang R(2024)Exploiting Group-Level Behavior Pattern for Session-Based RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328031036:1(152-166)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3280310
Ma YGan M(2024)Sequential-hierarchical attention network: Exploring the hierarchical intention feature in POI recommendationWorld Wide Web10.1007/s11280-024-01295-y27:6Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s11280-024-01295-y
TRUONG BPHO KDINH CMcALEER M(2021)ZERO-INFLATED POISSON REGRESSION MODELS: APPLICATIONS IN THE SCIENCES AND SOCIAL SCIENCESAnnals of Financial Economics10.1142/S201049522150006816:02(2150006)Online publication date: 28-Jun-2021
https://doi.org/10.1142/S2010495221500068
Wang CChen JZhou SShi QFeng YChen C(2021)SamWalker++: recommendation with informative sampling strategyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3102080(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3102080
Zhou BChen LZhou FLi SZhao SPan G(2021)Dynamic road crime risk prediction with urban open dataFrontiers of Computer Science10.1007/s11704-021-0136-z16:1Online publication date: 19-Oct-2021
https://doi.org/10.1007/s11704-021-0136-z
Sato MSingh JTakemori SSonoda TZhang QOhkuma THung CCerny TShin DBechini A(2020)Modeling user exposure with recommendation influenceProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3375760(1461-1464)Online publication date: 30-Mar-2020
https://dl.acm.org/doi/10.1145/3341105.3375760
Nguyen MCho Y(2020)A Hybrid Generative Model for Online User Behavior PredictionIEEE Access10.1109/ACCESS.2019.29625398(3761-3771)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2019.2962539
Wang CZhang MMa WLiu YMa S(2019)Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender SystemsThe World Wide Web Conference10.1145/3308558.3313594(1977-1987)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313594
Chen JWang CZhou SShi QFeng YChen C(2019)SamWalker: Social Recommendation with Informative Sampling StrategyThe World Wide Web Conference10.1145/3308558.3313582(228-239)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313582

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