research-article

Separating-Plane Factorization Models: Scalable Recommendation from One-Class Implicit Feedback

Authors:

Masoud ArdakaniAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 669 - 678

https://doi.org/10.1145/2983323.2983348

Published: 24 October 2016 Publication History

Abstract

We study the video recommendation problem based on a large amount of user viewing logs instead of explicit ratings. As viewing records are implicitly suggest user preferences, existing matrix factorization methods fail to generate discriminative recommendations based on such one-class positive samples. We propose a scalable approach called separating-plane matrix factorization (SPMF) to make effective recommendations based on positive implicit feedback, with a learning complexity that is comparable to traditional matrix factorization. With extensive offline evaluation in Tencent Data Warehouse (TDW) based on a large amount of data, we show that our approach outperforms a wide range of state-of-the-art methods. We also deployed our system in the QQ Browser App of Tencent and performed online A/B testing with real users. Results suggest that our approach increased the video click through rate by $23% over implicit-feedback collaborative filtering (IFCF), a scheme available in Apache Spark's MLlib.

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Cited By

Ko SLai HShuai HLee WYu PYang D(2020)Optimizing item and subgroup configurations for social-aware VR shoppingProceedings of the VLDB Endowment10.14778/3389133.338914313:8(1275-1289)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.14778/3389133.3389143
Esmeli RBader-El-Den MAbdullahi HHenderson D(2020)Improving Session-Based Recommendation Adopting Linear Regression-Based Re-ranking2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9207680(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/IJCNN48605.2020.9207680
Zhu NCao JLu XGu Q(2020)Leveraging pointwise prediction with learning to rank for top-N recommendationWorld Wide Web10.1007/s11280-020-00846-324:1(375-396)Online publication date: 23-Oct-2020
https://doi.org/10.1007/s11280-020-00846-3
Show More Cited By

Index Terms

Separating-Plane Factorization Models: Scalable Recommendation from One-Class Implicit Feedback
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

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Published: 24 October 2016

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Ko SLai HShuai HLee WYu PYang D(2020)Optimizing item and subgroup configurations for social-aware VR shoppingProceedings of the VLDB Endowment10.14778/3389133.338914313:8(1275-1289)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.14778/3389133.3389143
Esmeli RBader-El-Den MAbdullahi HHenderson D(2020)Improving Session-Based Recommendation Adopting Linear Regression-Based Re-ranking2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9207680(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/IJCNN48605.2020.9207680
Zhu NCao JLu XGu Q(2020)Leveraging pointwise prediction with learning to rank for top-N recommendationWorld Wide Web10.1007/s11280-020-00846-324:1(375-396)Online publication date: 23-Oct-2020
https://doi.org/10.1007/s11280-020-00846-3
Xu YZhu YShen YYu J(2018)Leveraging app usage contexts for app recommendation: a neural approachWorld Wide Web10.1007/s11280-018-0543-8Online publication date: 11-Apr-2018
https://doi.org/10.1007/s11280-018-0543-8
Li HGe YLian DLiu H(2017)Learning user's intrinsic and extrinsic interests for point-of-interest recommendationProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172182(2117-2123)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3172077.3172182
Zhu NCao J(2017)GTRM: A Top-N Recommendation Model for Smartphone Applications2017 IEEE International Conference on Web Services (ICWS)10.1109/ICWS.2017.124(309-316)Online publication date: Jun-2017
https://doi.org/10.1109/ICWS.2017.124

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