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Graph contrastive learning (GCL), as a popular self-supervised learning technique, has demonstrated promising capability in learning discriminative representations for diverse downstream tasks. A large body of GCL frameworks mainly work on graphs formed ...

Enthusiasm for “greening the financial system” is welcome, but a fundamental challenge remains: financial decision makers lack the necessary information. It is not enough to know that climate change is bad. Markets need credible, digestible information on ...

Modern cloud control plane infrastructure like Microsoft Azure has evolved into a complex one to serve customer needs for diverse types of services and adequate cloud-based resources. On such interconnected system, implementing changes at one ...

The main idea of multimedia recommendation is to introduce the profile content of multimedia documents as an auxiliary, so as to endow recommenders with generalization ability and gain better performance. However, recent studies using non-uniform ...

Recommender systems aim to increase user actions such as clicks and purchases. Typical evaluations of recommenders regard the purchase of a recommended item as a success. However, the item may have been purchased even without the recommendation. An ...

Trust in a Recommender System (RS) is crucial for its overall success. However, it remains underexplored whether users trust personal recommendation sources (i.e. other humans) more than impersonal sources (i.e. conventional RS), and, if they do, ...

Evaluating algorithmic recommendations is an important, but difficult, problem. Evaluations conducted offline using data collected from user interactions with an online system often suffer from biases arising from the user interface or the ...

Optimizing an interactive system against a predefined online metric is particularly challenging, especially when the metric is computed from user feedback such as clicks and payments. The key challenge is the counterfactual nature: in the case of Web ...

Evaluating and optimizing an interactive system (like search engines, recommender and advertising systems) from historical data against a predefined online metric is challenging, especially when that metric is computed from user feedback such as clicks ...