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

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    Type
    Conference Paper
    Authors
    Zhang, Chuxu
    Yu, Lu
    Zhang, Xiangliang cc
    Chawla, Nitesh
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    KAUST Grant Number
    2639
    Date
    2018-01-15
    Online Publication Date
    2018-01-15
    Print Publication Date
    2017-12
    Permanent link to this record
    http://hdl.handle.net/10754/627309
    
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    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.
    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.
    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.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2017 IEEE International Conference on Big Data (Big Data)
    DOI
    10.1109/bigdata.2017.8258001
    Additional Links
    http://ieeexplore.ieee.org/document/8258001/
    ae974a485f413a2113503eed53cd6c53
    10.1109/bigdata.2017.8258001
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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