Type
ArticleAuthors
Zhang, ChuxuYao, Huaxiu
Yu, Lu
Huang, Chao

Song, Dongjin
Chen, Haifeng
Jiang, Meng
Chawla, Nitesh V.
KAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Date
2021-05-25Online Publication Date
2021-05-25Print Publication Date
2021-07-26Permanent link to this record
http://hdl.handle.net/10754/669285
Metadata
Show full item recordAbstract
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:10.1145/3450353DOI
10.1145/3450353Additional Links
https://dl.acm.org/doi/10.1145/3450353ae974a485f413a2113503eed53cd6c53
10.1145/3450353