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развлекательные игры для детей на день рождения дома, 推荐系统的文章投Recsys还是ACM TORS？ - 知乎, 如何系统性的学习推荐系统？ - 知乎, 推荐系统这个名字英文是如何翻译的？ - 知乎, 推荐系统有哪些比较好的论文？ - 知乎, 2023年推荐系统的科研还有意义吗？ - 知乎, 最新深度强化学习推荐系统成果文章 - 知乎, 如何基于LLM做推荐系统？ - 知乎.

安全验证 - 知乎:root{--zhc-padding-horizontal:20px;--zhc-padding-vertical:16px;--zhc-notification-top:75px;--app-padding:16px;--app-header-height:52px;--app-max-width:640px;--app-width:1000px;--app-font-size:15px;}.css-55n9hh{position:fixed;top:0;right:0;left:0;z-index:101;display:none;height:2px;pointer-events:none;background:#1772F6;-webkit-transform:translateX(-100%);-ms-transform:translateX(-100%);transform:translateX(-100%);}, 选择投递推荐系统的文章到ACM RecSys会议还是ACM Transactions on Recommender Systems (TORS)期刊，取决于您的研究目标和时间框架。 ACM RecSys会议：是推荐系统领域的顶级会议之一，被CCF列为B类会议，具有较高的学术认可度。, A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item..

A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item., 5. Context-Aware Recommender Systems 基于上下文的推荐模型，现在不论是工业界还是学术界都非常火的一个topic； 6. Toward the next generation of recommender systems 对下一代推荐系统的一个综述； 7., 推荐系统本身太难了，所以大部分科研（论文）都对问题进行了简化。如果想做有意义的推荐问题，需要静下心来重新审视推荐问题的定义，场景，及评测方式，正视其挑战。可以读一下这篇文章（没有公式，没有算法）：Take a Fresh Look at Recommender Systems from an Evaluation Standpoint 总体上，我还是认为 , Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems. Lixin Zou, Long Xia, Zhuoye Ding, Jiaxing Song, Weidong Liu, Dawei Yin. KDD 2019. paper Environment reconstruction with hidden confounders for reinforcement learning based recommendation. Wenjie Shang, Yang Yu, Qingyang Li, Zhiwei Qin, Yiping Meng, Jieping Ye., 2. LLM in Rec 从应用视角出发，将LLM应用拆解到传统推荐系统的各个模块。参考自上交和华为合作的工作： How Can Recommender Systems Benefit from Large Language Models: A Survey。一般推荐系统都包括以下几个关键流程：数据采集：推荐系统展示结果给用户后，通过在线系统收集用户反馈数据，得到原始数据（raw , (IUI2019)Personalized Explanations for Hybrid Recommender Systems (RecSys2019)Explaining and Exploring Job Recommendations: a User-driven Approach for Interacting with Knowledge-based Job Recommender Systems.

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