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dc.contributor.authorZhang, Chuxu
dc.contributor.authorYu, Lu
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorChawla, Nitesh
dc.date.accessioned2018-03-15T06:26:39Z
dc.date.available2018-03-15T06:26:39Z
dc.date.issued2018-01-15
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.
dc.identifier.doi10.1109/bigdata.2017.8258001
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.
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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/8258001/
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.
dc.titleImWalkMF: Joint matrix factorization and implicit walk integrative learning for recommendation
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journal2017 IEEE International Conference on Big Data (Big Data)
dc.eprint.versionPost-print
dc.contributor.institutionUniversity of Notre Dame, Notre Dame, IN, USA
kaust.personYu, Lu
kaust.personZhang, Xiangliang
kaust.grant.number2639
refterms.dateFOA2018-06-14T03:41:29Z
dc.date.published-online2018-01-15
dc.date.published-print2017-12


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