EARS 2020:第三届可解释推荐和搜索国际研讨会

Yongfeng Zhang, Xu Chen, Yi Zhang, Min Zhang, C. Shah
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引用次数: 2

摘要

可解释的推荐和搜索试图开发模型或方法,不仅产生高质量的推荐或搜索结果,而且模型或结果的解释对于用户或系统设计者来说是可解释性的,这有助于提高系统的透明度、说服力、可信度和有效性等。这在个性化搜索和推荐场景中更为重要,用户想知道为什么特定的产品、网页、新闻报道或朋友建议会出现在他或她自己的搜索和推荐列表中。研讨会的重点是可解释的推荐、搜索和更广泛的红外任务的研究和应用。它将聚集该领域的研究人员和实践者进行讨论、思想交流和研究推广。它还将引发有关人工智能可解释性的最新法规的深刻辩论,涉及更广泛的社区,包括但不限于人工智能、机器学习、人工智能、数据科学等。
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EARS 2020: The 3rd International Workshop on ExplainAble Recommendation and Search
Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also interpretability of the models or explanations of the results for users or system designers, which can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness, etc. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The workshop focuses on the research and application of explainable recommendation, search, and a broader scope of IR tasks. It will gather researchers as well as practitioners in the field for discussions, idea communications, and research promotions. It will also generate insightful debates about the recent regulations regarding AI interpretability, to a broader community including but not limited to IR, machine learning, AI, Data Science, and beyond.
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