ImWalkMF: Joint matrix factorization and implicit walk integrative learning for recommendation

Handle URI:
http://hdl.handle.net/10754/627309
Title:
ImWalkMF: Joint matrix factorization and implicit walk integrative learning for recommendation
Authors:
Zhang, Chuxu; Yu, Lu; Zhang, Xiangliang ( 0000-0002-3574-5665 ) ; Chawla, Nitesh
Abstract:
Data sparsity and cold-start problems are prevalent in recommender systems. To address such problems, both the observable explicit social information (e.g., user-user trust connections) and the inferable implicit correlations (e.g., implicit neighbors computed by similarity measurement) have been introduced to complement user-item ratings data for improving the performances of traditional model-based recommendation algorithms such as matrix factorization. Although effective, (1) the utilization of the explicit user-user social relationships suffers from the weakness of unavailability in real systems such as Netflix or the issue of sparse observable content like 0.03% trust density in Epinions, thus there is no or little explicit social information that can be employed to improve baseline model in real applications; (2) the current similarity measurement approaches focus on inferring implicit correlations between a user (item) and their direct neighbors or top-k similar neighbors based on user-item ratings bipartite network, so that they fail to comprehensively unfold the indirect potential relationships among users and items. To solve these issues regarding both explicit/implicit social recommendation algorithms, we design a joint model of matrix factorization and implicit walk integrative learning, i.e., ImWalkMF, which only uses explicit ratings information yet models both direct rating feedbacks and multiple direct/indirect implicit correlations among users and items from a random walk perspective. We further propose a combined strategy for training two independent components in the proposed model based on sampling. The experimental results on two real-world sparse datasets demonstrate that ImWalkMF outperforms the traditional regularized/probabilistic matrix factorization models as well as other competitive baselines that utilize explicit/implicit social information.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Citation:
Zhang C, Yu L, Zhang X, Chawla N (2017) ImWalkMF: Joint matrix factorization and implicit walk integrative learning for recommendation. 2017 IEEE International Conference on Big Data (Big Data). Available: http://dx.doi.org/10.1109/bigdata.2017.8258001.
Publisher:
IEEE
Journal:
2017 IEEE International Conference on Big Data (Big Data)
KAUST Grant Number:
2639
Issue Date:
15-Jan-2018
DOI:
10.1109/bigdata.2017.8258001
Type:
Conference Paper
Sponsors:
This work is supported by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053 and the National Science Foundation (NSF) grant IIS-1447795. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. This publication is jointly supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. 2639.
Additional Links:
http://ieeexplore.ieee.org/document/8258001/
Appears in Collections:
Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorZhang, Chuxuen
dc.contributor.authorYu, Luen
dc.contributor.authorZhang, Xiangliangen
dc.contributor.authorChawla, Niteshen
dc.date.accessioned2018-03-15T06:26:39Z-
dc.date.available2018-03-15T06:26:39Z-
dc.date.issued2018-01-15en
dc.identifier.citationZhang C, Yu L, Zhang X, Chawla N (2017) ImWalkMF: Joint matrix factorization and implicit walk integrative learning for recommendation. 2017 IEEE International Conference on Big Data (Big Data). Available: http://dx.doi.org/10.1109/bigdata.2017.8258001.en
dc.identifier.doi10.1109/bigdata.2017.8258001en
dc.identifier.urihttp://hdl.handle.net/10754/627309-
dc.description.abstractData sparsity and cold-start problems are prevalent in recommender systems. To address such problems, both the observable explicit social information (e.g., user-user trust connections) and the inferable implicit correlations (e.g., implicit neighbors computed by similarity measurement) have been introduced to complement user-item ratings data for improving the performances of traditional model-based recommendation algorithms such as matrix factorization. Although effective, (1) the utilization of the explicit user-user social relationships suffers from the weakness of unavailability in real systems such as Netflix or the issue of sparse observable content like 0.03% trust density in Epinions, thus there is no or little explicit social information that can be employed to improve baseline model in real applications; (2) the current similarity measurement approaches focus on inferring implicit correlations between a user (item) and their direct neighbors or top-k similar neighbors based on user-item ratings bipartite network, so that they fail to comprehensively unfold the indirect potential relationships among users and items. To solve these issues regarding both explicit/implicit social recommendation algorithms, we design a joint model of matrix factorization and implicit walk integrative learning, i.e., ImWalkMF, which only uses explicit ratings information yet models both direct rating feedbacks and multiple direct/indirect implicit correlations among users and items from a random walk perspective. We further propose a combined strategy for training two independent components in the proposed model based on sampling. The experimental results on two real-world sparse datasets demonstrate that ImWalkMF outperforms the traditional regularized/probabilistic matrix factorization models as well as other competitive baselines that utilize explicit/implicit social information.en
dc.description.sponsorshipThis work is supported by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053 and the National Science Foundation (NSF) grant IIS-1447795. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. This publication is jointly supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. 2639.en
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/8258001/en
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.titleImWalkMF: Joint matrix factorization and implicit walk integrative learning for recommendationen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journal2017 IEEE International Conference on Big Data (Big Data)en
dc.eprint.versionPost-printen
dc.contributor.institutionUniversity of Notre Dame, Notre Dame, IN, USAen
kaust.authorYu, Luen
kaust.authorZhang, Xiangliangen
kaust.grant.number2639en
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