EARS 2020: The 3rd International Workshop on ExplainAble Recommendation and Search
EARS 2020: The 3rd International Workshop on ExplainAble Recommendation and Search
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DOI：
10.1145/3397271.3401468
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Yongfeng Zhang;Xu Chen;Yi Zhang;Min Zhang;C. Shah
中科院分区：
文献类型：
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作者：
Yongfeng Zhang;Xu Chen;Yi Zhang;Min Zhang;C. Shah
可解释的推荐和搜索试图开发模型或方法，不仅生成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的，这可以帮助提高系统的透明度，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加重要，其中用户想要知道为什么特定产品、网页、新闻报道或朋友建议存在于他或她自己的搜索和推荐列表中。该研讨会的重点是研究和可解释的建议，搜索和更广泛的IR任务的应用。它将聚集该领域的研究人员和从业人员进行讨论，交流想法和促进研究。它还将对最近关于人工智能可解释性的法规产生有见地的辩论，以更广泛的社区，包括但不限于IR，机器学习，人工智能，数据科学等。
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.