The fields of online behavior, attention economy, and recommendation systems are undergoing significant transformations. A common theme among these areas is the increasing importance of understanding and mitigating the negative externalities of excessive screen time, antisocial behavior, and biased recommendation algorithms. Researchers are exploring innovative approaches to identify and evaluate cognitive-behavioral fixation, develop new frameworks for assessing and regulating attention capture, and create more effective and efficient methods for cross-domain recommendation. Notable papers in these areas include Understanding Fanchuan in Livestreaming Platforms, No Such Thing as Free Brain Time, MARS, Unified Representation Learning, and Beyond Negative Transfer. The integration of large language models (LLMs) is also a key trend, with applications in review-driven recommendation, cross-pollination frameworks, and estimating positional bias in logged interaction data. Overall, these advancements have the potential to improve user experience, enhance recommendation quality, and promote a healthier online environment. Key directions in these fields include the recognition of attention as a scarce and valuable resource, the development of more effective methods for bias mitigation and algorithm adaptation, and the use of multimodal embeddings and semantic IDs to capture dynamic user interests and sequential patterns.