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    Inductive Contextual Relation Learning for Personalization

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    Type
    Article
    Authors
    Zhang, Chuxu
    Yao, Huaxiu
    Yu, Lu
    Huang, Chao cc
    Song, Dongjin
    Chen, Haifeng
    Jiang, Meng
    Chawla, Nitesh V.
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2021-05-25
    Online Publication Date
    2021-05-25
    Print Publication Date
    2021-07-26
    Permanent link to this record
    http://hdl.handle.net/10754/669285
    
    Metadata
    Show full item record
    Abstract
    Web personalization, e.g., recommendation or relevance search, tailoring a service/product to accommodate specific online users, is becoming increasingly important. Inductive personalization aims to infer the relations between existing entities and unseen new ones, e.g., searching relevant authors for new papers or recommending new items to users. This problem, however, is challenging since most of recent studies focus on transductive problem for existing entities. In addition, despite some inductive learning approaches have been introduced recently, their performance is sub-optimal due to relatively simple and inflexible architectures for aggregating entity’s content. To this end, we propose the inductive contextual personalization (ICP) framework through contextual relation learning. Specifically, we first formulate the pairwise relations between entities with a ranking optimization scheme that employs neural aggregator to fuse entity’s heterogeneous contents. Next, we introduce a node embedding term to capture entity’s contextual relations, as a smoothness constraint over the prior ranking objective. Finally, the gradient descent procedure with adaptive negative sampling is employed to learn the model parameters. The learned model is capable of inferring the relations between existing entities and inductive ones. Thorough experiments demonstrate that ICP outperforms numerous baseline methods for two different applications, i.e., relevant author search and new item recommendation.
    Citation
    Zhang, C., Yao, H., Yu, L., Huang, C., Song, D., Chen, H., … Chawla, N. V. (2021). Inductive Contextual Relation Learning for Personalization. ACM Transactions on Information Systems, 39(3), 1–22. doi:10.1145/3450353
    Publisher
    Association for Computing Machinery (ACM)
    Journal
    ACM Transactions on Information Systems
    DOI
    10.1145/3450353
    Additional Links
    https://dl.acm.org/doi/10.1145/3450353
    ae974a485f413a2113503eed53cd6c53
    10.1145/3450353
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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