Inductive Contextual Relation Learning for Personalization
| Autor: | Chao Huang, Lu Yu, Chuxu Zhang, Dongjin Song, Haifeng Chen, Huaxiu Yao, Meng Jiang, Nitesh V. Chawla |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Service (systems architecture)
Information retrieval Relation (database) Computer science Node (networking) 02 engineering and technology computer.software_genre General Business Management and Accounting Computer Science Applications Personalization News aggregator Ranking (information retrieval) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Relevance (information retrieval) Pairwise comparison computer Information Systems |
| Zdroj: | ACM Transactions on Information Systems. 39:1-22 |
| ISSN: | 1558-2868 1046-8188 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|