Comments (14)
Hi @morningsky, welcome! 欢迎参与到Datawhale社区~
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同意立项:),加油!期待~
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内容初步规划:初步规划TODO清单
这个打不开。
这个和fun-rec的区别是什么?对标什么库?deepctr吗?
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@finlay-liu
可以试一下打开链接之后,把https换成http;fun-rec是一个学习教程,该项目是一个框架,所有模型统一模块化封装,方便开发新模型与直接调用,使用方法类似deepctr;
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可否对框架支持的算法做一个明确的清单,先在一个框架里做事情,最后也好保证目标完成。
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@andongBlue 模型是一个增量的过程,总体还在规划中,每一块负责的人目前还没完全敲定,人员也在招募中,目前可能暂时无法给出;beta版本会覆盖核心方向(排序、召回、多任务),v0.1版本会对每个方向至少覆盖2个以上较新的模型,具体选择会依据该论文影响力/贡献者个人兴趣/贡献者实际需求综合而定
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同意
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@andongBlue 初步总体实现模型清单如下
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我觉得挺好的,这样可以建立反馈,同意立项
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问一哈,咱们这个和deeprec-torch, recbole这些类似的库的区别是在哪里?
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问一哈,咱们这个和deeprec-torch, recbole这些类似的库的区别是在哪里?
@YibinXie
设计上:与deepctr-torch相比,训练与模型解耦(与recbole类似),方便设计不同的Trainer(多任务、元学习、蒸馏学习等),以及继承第三方trainer。与recbole相比更轻量,可输入普通dataframe、dict等数据格式,方便用户自定义dataloader。
内容上:在包括少数经典召回排序模型的基础上,主要关注业界发表的更生态化的推荐论文模型(多任务、多兴趣、冷启动、延迟反馈等),进行统一复现与研究。与fun-rec推荐系统教程项目配套。
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该立项的最终结论是什么?
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你好 @morningsky,Datawhale 社区已经收到求助信息
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目前为持续招募相关人员中,如果人员充足,请在该评论下回复
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