Related Techniques: Link Prediction, Community Detection

Real-world graphs often exhibit noise and incompleteness due to limitations in data collection.

In order to gain a deeper understanding of our users, we propose a tri-level identity resolution approach known as 'Device-Person-Household’, supporting for a accurate targeting and effective user engagement strategy.

Link prediction techniques like CN, AA, RA(Zhou et al., EPJ’09) and SEAL(Zhang et al., NeuIPS’18) are employed to establish missing connections and classify relations.

Datasets and Codes:[EasyLink]

Related Techniques: Graph Neural Networks, Social Recommendation

Our research has demonstrated that social relations on Taobao exhibit homophily(the tendency of individuals to associate and bond with similar others), which can serve as an additional data source for user behavior modeling (Huang et al., SIGIR’21) and item sales modeling (Yang et al., TKDE’22).

To further boost the power of social relations in the recommendation, we employ graph structure learning(GSL) techniques on the social graph to generate superior recommendations for inactive users(Liu et al., DASFAA’24), and introduce GraphTranslator(Zhang et al., WWW’24) to conduct open-ended tasks on understanding users interests in social context with GNN(Graph Neural Network) and LLM(Large Language Model).

Datasets and Codes:[GraphTranslator] [LSIR]