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MFPR: A Personalized Ranking Recommendation with Multiple Feedback

Authors:

Philip S. YuAuthors Info & Claims

ACM Transactions on Social Computing, Volume 1, Issue 2

Article No.: 7, Pages 1 - 22

https://doi.org/10.1145/3216368

Published: 27 June 2018 Publication History

Abstract

Recently, recommender systems have played an important role in improving web user experiences and increasing profits. Recommender systems exploit users’ behavioral history (i.e., feedback on items) to build models. The feedback usually includes explicit feedback (e.g., ratings) and implicit feedback (e.g., browsing history, click logs), which are both useful for improving recommendations. However, as far as we are concerned, no existing works have integrated both explicit and multiple implicit feedback simultaneously. Therefore, we propose a unified and flexible model, named Multiple Feedback-based Personalized Ranking (MFPR), to make full use of multiple feedback, which uses a personalized ranking framework. To train model MFPR, we design an algorithm to generate ordered item pairs as labeled data, with consideration of both rating scores and multiple implicit feedback. Extensive experiments on two real-world datasets validate the effectiveness of the MFPR model. With the integration of multiple feedback, MFPR significantly improves recommendation performance.

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Cited By

Lee SLee ESeo Y(2023)An integration method for optimizing the use of explicit and implicit feedback in recommender systemsJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-023-04714-614:12(16995-17008)Online publication date: 13-Oct-2023
https://doi.org/10.1007/s12652-023-04714-6
Ma WChen XPan WMing ZSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)VAE++Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498436(666-674)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498436
Prathama FSenjaya WYahya BWu J(2021)Personalized recommendation by matrix co-factorization with multiple implicit feedback on pairwise comparisonComputers & Industrial Engineering10.1016/j.cie.2020.107033152(107033)Online publication date: Feb-2021
https://doi.org/10.1016/j.cie.2020.107033
Show More Cited By

Index Terms

MFPR: A Personalized Ranking Recommendation with Multiple Feedback
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms

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Published In

cover image ACM Transactions on Social Computing

ACM Transactions on Social Computing Volume 1, Issue 2

June 2018

102 pages

EISSN:2469-7826

DOI:10.1145/3234932

Editor:
Kevin Crowston
Syracuse University, USA

Issue’s Table of Contents

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2018

Accepted: 01 May 2018

Revised: 01 April 2018

Received: 01 May 2017

Published in TSC Volume 1, Issue 2

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Cited By

Lee SLee ESeo Y(2023)An integration method for optimizing the use of explicit and implicit feedback in recommender systemsJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-023-04714-614:12(16995-17008)Online publication date: 13-Oct-2023
https://doi.org/10.1007/s12652-023-04714-6
Ma WChen XPan WMing ZSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)VAE++Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498436(666-674)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498436
Prathama FSenjaya WYahya BWu J(2021)Personalized recommendation by matrix co-factorization with multiple implicit feedback on pairwise comparisonComputers & Industrial Engineering10.1016/j.cie.2020.107033152(107033)Online publication date: Feb-2021
https://doi.org/10.1016/j.cie.2020.107033
Jiang QHe L(2021)Method of Online Teaching Resource Recommendation Towards International Communication Based on.NET Platforme-Learning, e-Education, and Online Training10.1007/978-3-030-84383-0_10(110-121)Online publication date: 5-Aug-2021
https://doi.org/10.1007/978-3-030-84383-0_10
Zang ZYang X(2020)Feedback Knowledge Graph for RecommendationProceedings of the 2020 2nd International Conference on Big-data Service and Intelligent Computation10.1145/3440054.3440061(37-41)Online publication date: 3-Dec-2020
https://dl.acm.org/doi/10.1145/3440054.3440061
Chen XLi LPan WMing Z(2020)A Survey on Heterogeneous One-class Collaborative FilteringACM Transactions on Information Systems10.1145/340252138:4(1-54)Online publication date: 11-Aug-2020
https://dl.acm.org/doi/10.1145/3402521
Li G(2020)Personalized recommendation method of Ideological and political course resources based on user model2020 International Conference on Robots & Intelligent System (ICRIS)10.1109/ICRIS52159.2020.00171(683-687)Online publication date: Nov-2020
https://doi.org/10.1109/ICRIS52159.2020.00171

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