• Login
    View Item 
    •   Home
    • Research
    • Conference Papers
    • View Item
    •   Home
    • Research
    • Conference Papers
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    In2Rec: Influence-based Interpretable Recommendation

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    lp1303-liuA[2].pdf
    Size:
    865.0Kb
    Format:
    PDF
    Description:
    Accepted manuscript
    Download
    Type
    Conference Paper
    Authors
    Liu, Huafeng
    Yu, Jian
    Wen, Jingxuan
    Zhang, Xiangliang
    Jing, Liping
    Zhang, Min
    KAUST Department
    King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
    Date
    2019-11-04
    Online Publication Date
    2019-11-04
    Print Publication Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/660624
    
    Metadata
    Show full item record
    Abstract
    Interpretability of recommender systems has caused increasing attention due to its promotion of the effectiveness and persuasiveness of recommendation decision, and thus user satisfaction. Most existing methods, such as Matrix Factorization (MF), tend to be black-box machine learning models that lack interpretability and do not provide a straightforward explanation for their outputs. In this paper, we focus on probabilistic factorization model and further assume the absence of any auxiliary information, such as item content or user review. We propose an influence mechanism to evaluate the importance of the users' historical data, so that the most related users and items can be selected to explain each predicted rating. The proposed method is thus called Influence-based Interpretable Recommendation model (In2Rec). To further enhance the recommendation accuracy, we address the important issue of missing not at random, i.e., missing ratings are not independent from the observed and other unobserved ratings, because users tend to only interact what they like. In2Rec models the generative process for both observed and missing data, and integrates the influence mechanism in a Bayesian graphical model. A learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to maximum a posteriori estimation for In2Rec. A series of experiments on four real-world datasets (Movielens 10M, Netflix, Epinions, and Yelp) have been conducted. By comparing with the state-of-the-art recommendation methods, the experimental results have shown that In2Rec can consistently benefit the recommendation system in both rating prediction and ranking estimation tasks, and friendly interpret the recommendation results with the aid of the proposed influence mechanism.
    Citation
    Liu, H., Wen, J., Jing, L., Yu, J., Zhang, X., & Zhang, M. (2019). In2Rec. Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM ’19. doi:10.1145/3357384.3358017
    Sponsors
    This work was supported in part by the National Natural Science Foundation of China under Grant 61822601, 61773050, 61672311 and 61632004; the Beijing Natural Science Foundation under Grant Z180006; the Beijing Municipal Science & Technology Commission under Grant Z181100008918012.
    Publisher
    Association for Computing Machinery (ACM)
    Conference/Event name
    28th ACM International Conference on Information and Knowledge Management, CIKM 2019
    DOI
    10.1145/3357384.3358017
    Additional Links
    http://dl.acm.org/citation.cfm?doid=3357384.3358017
    ae974a485f413a2113503eed53cd6c53
    10.1145/3357384.3358017
    Scopus Count
    Collections
    Conference Papers

    entitlement

     
    DSpace software copyright © 2002-2022  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.