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This document is a call for a new era of artificial intelligence (AI) in the connected world, specifically focusing on the field of Web Intelligence (WI). WI has evolved over the past 20 years, with three significant stages: WI 1.0 (Wisdom Web), WI 2.0 (Wisdom Web of Thing), and WI 3.0 (Wisdom Web of Everything). The document highlights several papers published in the Web Intelligence journal that explore different aspects of WI, including its impact on a better connected world, cross-cutting areas with brain informatics, biases in recommender systems, the influence of ChatGPT on information retrieval, the role of big data in the retail industry, order pairing mechanisms in real-time ride-sharing, data replication in cloud systems, and security risks in IoT-fog architecture. The document calls for more collaboration and communication among researchers in the WI field and emphasizes the importance of global interactions for co-intelligence and co-creation. [Extracted from the article]

User feedback data (e.g., clicks, dwell time in the product detail page) have been incorporated in the training process of many ranking models for better performance. Such approaches are widely used in many ranking applications, including search and recommendation. Recently, the inherent biases in user feedback data have been studied, which indicates how the users' behaviors can be affected by factors other than relevancy. By identifying and removing these biases, the ranking models can be further improved. Researchers have developed a variety of debiasing methods on different bias factors. Most of them only focus on one type of bias and pay little attention to different types of bias from a unified perspective. In this paper, we conduct a comprehensive study of bias focusing on the application of ranking problems in recommender systems which is highly important for the research of web intelligence. Then, we share our experiences derived from designing and optimizing unbiased models to improve feeds recommendation. To uncover the effects of biases and achieve better ranking performance, we propose several unbiased models and compare with state-of-the-art models. We conduct extensive offline experiments on real datasets and validate the effectiveness of our method by performing online A/B testing in a real-world recommender system. [ABSTRACT FROM AUTHOR]