第四届在线推荐系统和用户建模研讨会论文集-- ORSUM 2021
Proceedings of the 4th Workshop on Online Recommender Systems and User Modeling -- ORSUM 2021
摘要：
现代在线服务以非常快的速度不断生成数据。这种连续的数据流包含内容-例如，帖子、新闻、产品、评论，还有用户反馈，例如，评级、视图、读取、点击-连同上下文数据-用户设备、空间或时间数据、用户任务或活动、天气。考虑到内容、上下文和用户偏好或意图的连续和潜在快速变化，这对于被设计成批量训练的系统和算法来说可能是压倒性的。因此，重要的是调查在线方法能够透明地适应在线服务的内在动态。从数据流中学习的增量模型在推荐系统社区中越来越受到关注，因为它们具有处理动态复杂环境中生成的连续数据流的自然能力。用户建模和个性化可以特别受益于能够增量和在线维护模型的算法。本次研讨会的目的是促进贡献，并汇集了越来越多的研究人员和从业者感兴趣的在线，自适应的方法来用户建模，推荐和个性化，以及他们对多个维度的影响，如评估，再现性，隐私和可解释性。
英文摘要：
Modern online services continuously generate data at very fast rates. This continuous flow of data encompasses content - e.g., posts, news, products, comments -, but also user feedback - e.g., ratings, views, reads, clicks -, together with context data - user device, spatial or temporal data, user task or activity, weather. This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate online methods able to transparently adapt to the inherent dynamics of online services. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with the continuous flows of data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online. The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, and their implications regarding multiple dimensions, such as evaluation, reproducibility, privacy and explainability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
