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https://api.github.com/repos/datawhalechina/fun-rec/issues/117 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/117/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/117/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/117/events | https://github.com/datawhalechina/fun-rec/issues/117 | 2,719,687,538 | I_kwDOEQ6RSs6iGyNy | 117 | 矩阵分解图片无法加载 | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/116 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/116/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/116/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/116/events | https://github.com/datawhalechina/fun-rec/issues/116 | 2,620,912,045 | I_kwDOEQ6RSs6cN_Gt | 116 | 新闻数据集不存在了 | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/115 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/115/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/115/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/115/events | https://github.com/datawhalechina/fun-rec/issues/115 | 2,602,684,566 | I_kwDOEQ6RSs6bIdCW | 115 | ItemCF中的资料指代不明 | {
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} | [] | open | false | null | [] | null | [] | 2024-10-21T14:09:55 | 2024-10-21T14:09:55 | null | NONE | null | 如下图，其中的KDD2020去偏商品推荐没有给出相应资料链接，同时KDD2020论文集貌似没有出现”debias“字样的论文，求解惑，谢谢！
| null | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/114 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/114/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/114/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/114/events | https://github.com/datawhalechina/fun-rec/issues/114 | 2,538,248,480 | I_kwDOEQ6RSs6XSpkg | 114 | 请教MIND算法那个胶囊网络的问题 | {
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} | [] | open | false | null | [] | null | [] | 2024-09-20T08:48:18 | 2024-09-20T08:48:18 | null | NONE | null | 你好，感谢你的文档，这是我读过的讲的最清楚的版本。
不过我还是有个疑问，在MIND算法里的胶囊网络那块，为何对 wij做softmax的时候，是在胶囊个数维度，而不是在输入向量维度？
因为你讲单个胶囊的时候，我看softmax是在输入向量维度做的。但是到了介绍论文那里，就变成了胶囊维度了。MIND的代码里
也都是按照胶囊维度。不太明白。 | null | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/113 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/113/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/113/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/113/events | https://github.com/datawhalechina/fun-rec/issues/113 | 2,516,087,322 | I_kwDOEQ6RSs6V-HIa | 113 | Swing 部分代码的数据集 | {
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} | [] | open | false | null | [] | null | [] | 2024-09-10T10:46:07 | 2024-09-10T10:52:03 | null | NONE | null | Swing 中用于练习的代码使用的数据集为“ratings_final” 但是没有在dataset里找到对应的数据集
if __name__ == "__main__":
train_data_path = "./ratings_final.txt"
item_sim_save_path = "./item_sim_dict.txt"
top_k = 10 #与item相似的前 k 个item
train = load_data(train_data_path)
u_items, i_users = get_uitems_iusers(train)
item_sim_dict = swing_model(u_items, i_users)
save_item_sims(item_sim_dict, top_k, item_sim_save_path)
| null | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/112 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/112/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/112/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/112/events | https://github.com/datawhalechina/fun-rec/issues/112 | 2,499,178,854 | I_kwDOEQ6RSs6U9nFm | 112 | 数据集问题 | {
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} | [] | open | false | null | [] | null | [] | 2024-09-01T02:49:45 | 2024-09-01T02:49:45 | null | NONE | null | 请问一下dssm里面的两个数据集去哪里获取呢 | null | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/111 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/111/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/111/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/111/events | https://github.com/datawhalechina/fun-rec/issues/111 | 2,477,182,327 | I_kwDOEQ6RSs6Tps13 | 111 | 为什么`key_masks`等于` tf.not_equal(keys[:,:,0], 0) ` | {
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"是因为mask向量的值都是0，而正常的向量的值，肯定不是0，所以，就选择Emb中的第一位来代替，是这样吗？"
] | 2024-08-21T06:30:47 | 2024-08-21T06:33:49 | null | NONE | null | 请问一下各路大神，以下代码细节：
```python
class AttentionPoolingLayer(Layer):
def __init__(self, att_hidden_units=(256, 128, 64)):
super(AttentionPoolingLayer, self).__init__()
self.att_hidden_units = att_hidden_units
self.local_att = LocalActivationUnit(self.att_hidden_units)
def call(self, inputs):
# keys: B x len x emb_dim, queries: B x 1 x emb_dim
queries, keys = inputs
# 获取行为序列embedding的mask矩阵，将Embedding矩阵中的非零元素设置成True，
key_masks = tf.not_equal(keys[:,:,0], 0) # B x len
```
**key_masks = tf.not_equal(keys[:,:,0], 0) # B x len**，为啥是`tf.not_equal(keys[:,:,0], 0)`的值，当做`key_masks`呢？百思不得其解 | null | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/110 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/110/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/110/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/110/events | https://github.com/datawhalechina/fun-rec/issues/110 | 2,437,517,984 | I_kwDOEQ6RSs6RSZKg | 110 | 没有找到相关代码 | {
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"文章结尾处写道”详细代码见尾部GitHub链接“也并没有给出代码地址。\r\n\r\n",
"> 文章结尾处写道”详细代码见尾部GitHub链接“也并没有给出代码地址。 \r\n\r\n代码地址，应该指的是这个repo中的`codes`目录中的代码"
] | 2024-07-30T10:45:24 | 2024-08-21T06:28:43 | null | NONE | null | 在ch2.1.2章节（YoutubeDNN）中提到，详情代码见“点击日志数据集初步处理与采样.ipynb"和”EDA与数据预处理.ipynb“，但是在fun-rec项目文件中并没有看到相关代码 | null | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/109 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/109/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/109/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/109/events | https://github.com/datawhalechina/fun-rec/issues/109 | 2,407,342,898 | I_kwDOEQ6RSs6PfSMy | 109 | ch2.1.1中图片链接错误 | {
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[链接](https://datawhalechina.github.io/fun-rec/#/ch02/ch2.1/ch2.1.1/mf?id=%e9%9a%90%e8%af%ad%e4%b9%89%e6%a8%a1%e5%9e%8b)
图片错误：
右击点击链接可以发现该图片的地址是：
https://img-blog.csdnimg.cn/20200822212051499.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3d1emhvbmdxaWFuZw==,size_1,color_FFFFFF,t_70#pic_center
而实际上直接进去会是：**403 Forbidden**
因此图片地址应该修改为：
https://img-blog.csdnimg.cn/20200822212051499.png
也就是**去掉查询字符串**。并且x-oss-process=image/watermark是要对图像加水印，其余参数估计是调整水印的。 | null | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/108 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/108/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/108/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/108/events | https://github.com/datawhalechina/fun-rec/issues/108 | 2,374,123,449 | I_kwDOEQ6RSs6Ngj-5 | 108 | 涉及到的数据文件在哪里可以找到？ | {
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"https://github.com/datawhalechina/fun-rec/issues/106\r\n参考这个\r\n"
] | 2024-06-26T03:41:03 | 2024-07-12T08:38:21 | 2024-07-12T08:38:21 | NONE | null | 
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https://api.github.com/repos/datawhalechina/fun-rec/issues/107 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/107/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/107/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/107/events | https://github.com/datawhalechina/fun-rec/pull/107 | 2,345,866,477 | PR_kwDOEQ6RSs5yFPjU | 107 | 非常不错的项目 | {
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} | [] | open | false | null | [] | null | [] | 2024-06-11T09:18:30 | 2024-06-11T09:18:30 | null | NONE | null | 不知道后续有没有考虑生成式推荐系统的一些工作呢， | null | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/106 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/106/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/106/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/106/events | https://github.com/datawhalechina/fun-rec/issues/106 | 2,263,681,065 | I_kwDOEQ6RSs6G7Qgp | 106 | Swing那一节的代码的txt文件在哪，没有找到 | {
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"朋友您好，感谢关注Fun Rec的工作，这个txt文件其实可以根据您自己的数据集来设置，整个算法的输入其实是一个pandas dataframe的结构，有三列分别为userid,itemid,rate。\r\n为了方便学习也提供个示例数据给您\r\n[ratings_final.txt](https://github.com/datawhalechina/fun-rec/files/15126826/ratings_final.txt)\r\n",
"nice~",
"好的谢谢"
] | 2024-04-25T14:03:05 | 2024-07-12T08:38:12 | 2024-07-12T08:38:12 | NONE | null | 
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https://api.github.com/repos/datawhalechina/fun-rec/issues/105 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/105/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/105/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/105/events | https://github.com/datawhalechina/fun-rec/issues/105 | 2,230,180,311 | I_kwDOEQ6RSs6E7dnX | 105 | 请问概率计算函数是怎么定义的呢 | {
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"还有下文的SampleOneProb方法，整个仓库中好像都没有相关的定义和导入代码，辛苦问下因该在哪里找到相关的详细定义呢😅\r\n\r\n",
"不好意思额，这个代码貌似我也没找到额，我尝试看能不能找到当年写这个文档的同学吧"
] | 2024-04-08T03:31:26 | 2024-04-26T06:43:07 | null | NONE | null | 在 DSSM 模型的实战代码中，负样本采样时使用了一个概率计算函数cal_pos，但是在上下文中好像没有定义和导入的操作，想问一下这个函数具体是怎么定义的呢？
<img width="1398" alt="image" src="https://github.com/datawhalechina/fun-rec/assets/44490800/e113bdfd-722c-4e49-8d1e-0a8ac755f8cc">
| null | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/104 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/104/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/104/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/104/events | https://github.com/datawhalechina/fun-rec/issues/104 | 2,185,606,769 | I_kwDOEQ6RSs6CRbZx | 104 | 无数bug，慎入 | {
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"HI，能问一下，是哪里的bug比较多吗？我们看怎么改进一下\r\n",
"有任何问题请加微信联系，微信号为：LOOrze"
] | 2024-03-14T07:12:10 | 2024-03-20T14:27:58 | 2024-03-20T14:27:58 | NONE | null | 无数bug，慎入！！！
无数bug，慎入！！！
无数bug，慎入！！！ | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/103 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/103/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/103/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/103/events | https://github.com/datawhalechina/fun-rec/issues/103 | 2,185,242,072 | I_kwDOEQ6RSs6CQCXY | 103 | 新闻推荐竞赛环境问题 | {
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"不好意思，都是好多年前的了\r\n\r\n目前问题比较多的可能是tf版本的问题，之前用的是tf1.15，其他的大概率没啥问题"
] | 2024-03-14T02:14:27 | 2024-03-15T03:36:15 | 2024-03-15T03:36:15 | NONE | null | 可以给个requirements.txt 吗 | {
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"应该是markdown复制的时候出的问题，问题已修复，感谢反馈"
] | 2024-02-26T02:24:52 | 2024-03-15T03:44:44 | 2024-03-15T03:44:44 | NONE | null | 复制了 ItemCF 的代码，运行出现了以下错误：
看了代码发现是 target_user 有问题，这里 Alice 前后有空格，导致读不到正常的 user
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https://api.github.com/repos/datawhalechina/fun-rec/issues/100 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/100/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/100/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/100/events | https://github.com/datawhalechina/fun-rec/issues/100 | 2,049,926,577 | I_kwDOEQ6RSs56L2Wx | 100 | 公式错误 | {
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"不好意思啊，能了解一下是哪一行有问题吗",
"> 不好意思啊，能了解一下是哪一行有问题吗\r\n\r\n在这里[第三章 推荐算法面经/3.1 ML与DL基础.md](https://github.com/datawhalechina/fun-rec/pull/99/commits/cab292094c2da6a9c7c10a6bad6190dac3378842#diff-9a41ce3701f6b1843926417d9b6c04a3984a821c070d2be1f1f8ca1349460e12)\r\n我在这里提交了一个pr #99 "
] | 2023-12-20T06:34:04 | 2024-03-16T05:29:29 | null | NONE | null | 在面试题部分，逻辑回归推导有一步错误表示 | null | {
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"1、youtubednn应该是不可以加item其他维度的特征了，这里写错了\r\n2、item特征应该是无法直接使用到youtube里面的，这里的example_age，应该指的是样本的特征，可以参考下面的文章\r\n\r\n\r\n链接：https://zhuanlan.zhihu.com/p/52504407\r\n"
] | 2023-12-12T01:56:49 | 2024-01-08T08:03:56 | 2024-01-08T08:03:56 | NONE | null | 
这里提到可以增加item的特征, 但是我在使用时出现报错 "Now YoutubeNN only support 1 item feature like item_id", 使用的版本是deepmatch(0.3.1), deepctr(0.9.3)
另一个问题是example_age的使用, 我理解这是一个item 的特征, 那么我是不是可以将其他的特征封装到user_feature_columns里面呢 | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/97 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/97/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/97/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/97/events | https://github.com/datawhalechina/fun-rec/issues/97 | 2,000,523,915 | I_kwDOEQ6RSs53PZKL | 97 | 【资料自荐】fun-rec项目或许可以加入如下repo | {
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"加微信聊一下，我的微信是LOOrze"
] | 2023-11-18T18:12:57 | 2024-01-10T15:13:54 | 2024-01-10T15:13:54 | NONE | null | 同学你好，很高兴能够看到这个开源项目能够帮助大家入门推荐系统。
在此，希望自荐我的个人开源项目：https://github.com/Doragd/Algorithm-Practice-in-Industry
询问是否能够加入到fun-rec项目中
【为什么想加入】：推荐系统是一个非常偏工业界实践的领域，除开基础知识和代码实践外，我觉得非常有用的资料是各个大厂的技术分享文章以及搜广推顶会上的文章。因此，我认为我的项目或许能够帮助fun-rec完善这一部分。
【我的项目是什么】：我的项目收集了：
* 知乎上的关于搜广推算法系列串讲/经典问题
* 各个大厂的搜广推实践分享，大概有400多篇吧（2017-2023）
* 搜广推顶会列表以及翻译：2012-2023，涵盖了sigir/www/recsys/kdd/cikm/wsdm等
希望同学可以不吝赐教我的项目：https://github.com/Doragd/Algorithm-Practice-in-Industry
希望以一种合适的方式能够加入fun-rec中，为这个项目添砖加瓦。
此致，
Gordon Lee | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/95 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/95/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/95/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/95/events | https://github.com/datawhalechina/fun-rec/issues/95 | 1,846,136,810 | I_kwDOEQ6RSs5uCc_q | 95 | 请问这个package是什么？ | {
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"不好意思，这个好像是自己写的包，貌似忘记传了，我们再找找\r\n\r\n有问题欢迎首页加微信聊",
"@ruyiluo 感谢"
] | 2023-08-11T03:15:57 | 2023-09-21T09:30:25 | 2023-09-10T11:04:47 | NONE | null | 在`codes/news_recsys/news_rec_server/recprocess/recall/u2i_usercf.ipynb` notebook里有这个依赖：
```python
from metric import PrintMetric
```
但是我找不到正确的metric包，我用`pip install metric`安装的metric包里没有`PrintMetric`。
请问这个包在哪里下载？
谢谢啦 | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/94 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/94/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/94/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/94/events | https://github.com/datawhalechina/fun-rec/issues/94 | 1,778,082,736 | I_kwDOEQ6RSs5p-2Ow | 94 | 大佬你好，请问一下文本推荐系统的YouTubeDNN模型召回那里，gen_data_set里面的test_set的label为什么是0啊 | {
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"1. youtubednn模型对应的样本是不需要显示的构造负样本的，所以样本中都是用户点击的，也就是整样本\r\n2. 你看到代码中往train_data中塞负样本，是因为该函数也被用来构造双塔模型的样本，对于双塔模型来说，一般是显示的构造负样本\r\n3. 最终对于youtube构造的样本来说，其函数对应的负样本参数设置的是0\r\n\r\n有问题欢迎首页加微信聊"
] | 2023-06-28T02:52:49 | 2023-09-10T11:03:08 | 2023-09-10T11:03:08 | NONE | null | 大佬你好，请问一下文本推荐系统的YouTubeDNN模型召回那里，gen_data_set里面的test_set的label为什么是0啊 | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/93 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/93/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/93/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/93/events | https://github.com/datawhalechina/fun-rec/issues/93 | 1,758,886,622 | I_kwDOEQ6RSs5o1nre | 93 | 感觉文档有些错乱和丢失，比如"深度学习模型搭建基础"一节对于理解理解源码很重要。有空闲时间，怎么贡献力量 | {
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"项目目前正在改版中，首页加微信，私聊我，进一步详聊一下\r\n\r\n"
] | 2023-06-15T13:49:42 | 2023-09-10T11:05:11 | 2023-09-10T11:05:11 | NONE | null | null | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/92 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/92/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/92/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/92/events | https://github.com/datawhalechina/fun-rec/pull/92 | 1,683,046,158 | PR_kwDOEQ6RSs5PGNEj | 92 | 修复了图片、链接等错误 | {
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- chore: ryluo-oss 下链接使用https 43151d0cdf930ce0cb2d04115cb2f6e00bd0d0c2 | null | {
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"近期会对所有内容重新梳理和总结，会解决这些问题，多细反馈"
] | 2023-03-02T06:05:03 | 2023-03-10T04:00:17 | 2023-03-10T04:00:17 | NONE | null | 下图关于NFM 的图片链接错了
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"\r\n第三个求和号小于等于号",
"同问"
] | 2022-12-08T02:50:13 | 2023-11-04T18:45:08 | null | NONE | null | 
| null | {
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"首页readme文档最上面不是显示了么"
] | 2022-10-20T11:55:28 | 2022-10-27T09:41:00 | 2022-10-27T09:41:00 | NONE | null | 能问一下项目开发环境的版本配置是怎么样的？比如tensorflow等的版本。 | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/86 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/86/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/86/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/86/events | https://github.com/datawhalechina/fun-rec/issues/86 | 1,369,712,786 | I_kwDOEQ6RSs5RpCiS | 86 | 在线文档的公式渲染 | {
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"已经修复了"
] | 2022-09-12T11:19:18 | 2022-09-17T07:27:35 | 2022-09-17T07:27:35 | NONE | null | <img width="699" alt="image" src="https://user-images.githubusercontent.com/77790779/189640614-183a22b4-337f-4bb8-aa32-10abd2d91f4e.png">
在线文档的公式渲染有些问题 | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/85 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/85/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/85/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/85/events | https://github.com/datawhalechina/fun-rec/issues/85 | 1,361,074,945 | I_kwDOEQ6RSs5RIFsB | 85 | 不清楚 | {
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"s2指的是，基于i和j的聚类类别算出来的相似度。\r\n<img width=\"937\" alt=\"image\" src=\"https://user-images.githubusercontent.com/27343352/221466944-a9f292d5-65ef-482a-9107-49dbbd47a0ff.png\">\r\n其中L(i),L(j)为i,j的聚类类别\r\n总的相似度定义为两种层级相似度的结合"
] | 2022-09-04T08:02:51 | 2023-03-03T05:15:15 | 2023-03-03T05:15:15 | NONE | null | 
这个s2是指什么，是Swing吗，
https://datawhalechina.github.io/fun-rec/#/ch02/ch2.1/ch2.1.1/Swing | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/84 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/84/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/84/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/84/events | https://github.com/datawhalechina/fun-rec/issues/84 | 1,339,961,071 | I_kwDOEQ6RSs5P3i7v | 84 | 4.1 ML与DL基础 | {
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"有一种经验说法认为boosting对过拟合有一定的稳健性，不太容易过拟合，泛化能力不错。这种稳健性不是源于boosting(boosting本身是会加剧overfit的），而是源于弱模型合成（加权平均）过程中的“投票”效果，而且，我们总是可以使用交叉验证，及时停止boosting，避免合成过多的模型\r\n\r\n参考链接：https://zhuanlan.zhihu.com/p/517900957"
] | 2022-08-16T07:57:18 | 2022-09-17T08:03:21 | 2022-09-17T08:03:21 | NONE | null | `bagging与boosting分别从什么角度降低过拟合？
bagging降低方差，boosting降低方差`
bagging降低variance。boosting降低bias来提升预测精度。
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https://api.github.com/repos/datawhalechina/fun-rec/issues/83 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/83/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/83/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/83/events | https://github.com/datawhalechina/fun-rec/issues/83 | 1,303,286,693 | I_kwDOEQ6RSs5NrpOl | 83 | 新闻推荐数据集 | {
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"这个数据是科大讯飞的一个比赛数据集，你去看一下他们官网上对应的是那一个竞赛吧，最好咨询一下他们工作人员，我们不确定能不能在论文中引用",
"谢谢"
] | 2022-07-13T11:15:44 | 2022-09-17T16:02:20 | 2022-09-17T01:25:33 | NONE | null | 请问新闻推荐数据集在哪里公开的，可以在论文中引用吗？ | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/82 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/82/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/82/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/82/events | https://github.com/datawhalechina/fun-rec/pull/82 | 1,292,325,626 | PR_kwDOEQ6RSs46wSmB | 82 | fix ch2.1.1 | {
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"#81 ",
"我这边已经修正了，哈哈，没看到你还提PR了"
] | 2022-07-03T14:32:46 | 2022-07-24T02:43:52 | 2022-07-24T02:43:52 | NONE | null | 
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https://api.github.com/repos/datawhalechina/fun-rec/issues/81 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/81/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/81/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/81/events | https://github.com/datawhalechina/fun-rec/issues/81 | 1,292,089,800 | I_kwDOEQ6RSs5NA7nI | 81 | ItemCF中手算实例符号是否错误 | {
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"OK，已修正"
] | 2022-07-02T16:25:44 | 2022-07-24T02:41:49 | 2022-07-24T02:41:49 | NONE | null | 
如图是否应该为乘号？ | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/80 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/80/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/80/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/80/events | https://github.com/datawhalechina/fun-rec/issues/80 | 1,275,008,531 | I_kwDOEQ6RSs5L_xYT | 80 | 版本问题 | {
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"你指的是哪里代码的deepctr的版本?具体是哪个文件呢？",
"codes下厨除了base_model文件下的所有代码使用tf 1.14.0 gpu 版本外（以及新闻推荐竞赛相关的代码），其他的代码都使用tf2.2.x gpu版就行\r\n"
] | 2022-06-17T13:05:36 | 2022-07-24T02:31:58 | 2022-07-24T02:31:58 | NONE | null | deepctr 和tensorflow的版本是多少 | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/79 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/79/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/79/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/79/events | https://github.com/datawhalechina/fun-rec/issues/79 | 1,263,368,272 | I_kwDOEQ6RSs5LTXhQ | 79 | Typo | {
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"https://github.com/datawhalechina/fun-rec/blob/master/docs/ch01/ch1.3.md\r\n\r\n\r\n\r\nSaprk -> Spark",
"OK，已经修正"
] | 2022-06-07T14:07:57 | 2022-07-24T02:35:53 | 2022-07-24T02:35:53 | NONE | null | https://github.com/datawhalechina/fun-rec/blob/master/docs/ch01/ch1.2.md
<img width="1027" alt="image" src="https://user-images.githubusercontent.com/29893343/172399365-91b4bde8-3a6a-470a-9fc7-5db3bbca4228.png">
是应该是分布式框架？ | {
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https://api.github.com/repos/datawhalechina/fun-rec/issues/78 | https://api.github.com/repos/datawhalechina/fun-rec | https://api.github.com/repos/datawhalechina/fun-rec/issues/78/labels{/name} | https://api.github.com/repos/datawhalechina/fun-rec/issues/78/comments | https://api.github.com/repos/datawhalechina/fun-rec/issues/78/events | https://github.com/datawhalechina/fun-rec/pull/78 | 1,252,936,460 | PR_kwDOEQ6RSs44saEl | 78 | Add files via upload | {
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"OK!"
] | 2022-05-30T16:20:07 | 2022-05-31T13:39:05 | 2022-05-31T13:38:57 | CONTRIBUTOR | null | add item2vec.md | {
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"OK，已经修复"
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<img width="959" alt="image" src="https://user-images.githubusercontent.com/11362493/170053056-1d9d98b4-ef2b-424d-b4df-86a83b55ddfb.png">
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"是修改了一些错别字和不通顺的地方，高亮句子里面细节部分不一样。"
] | 2022-04-16T13:33:24 | 2022-05-27T06:29:49 | 2022-04-23T01:58:34 | CONTRIBUTOR | null | fixed some typos. | {
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"可能版本太高了，我记得当初使用的tf版本是tf2.0或者tf1.14",
"> 可能版本太高了，我记得当初使用的tf版本是tf2.0或者tf1.14\r\n\r\n\r\n我看deepmatch是支持2.0+的，是因为deepmatch的版本问题吗 可以给一下requirement.txt吗 QAQ\r\n\r\n",
"换成tf1.14大概率没啥问题，这个印象比较深刻，主要就是tf的版本问题，其他的都没问题的",
"这个环境太早了，我准备已经没有那个环境了",
"qaq 谢谢大佬了"
] | 2022-04-14T02:34:25 | 2022-04-15T02:52:42 | 2022-04-14T05:02:12 | NONE | null |
> model = YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=5, user_dnn_hidden_units=(64, embedding_dim))
出现bug [Could not build a TypeSpec for <KerasTensor: shape=(None, 16) dtype=float32 (created by layer 'tf.compat.v1.squeeze_3')> with type KerasTensor]()
是keras和TensorFlow版本问题吗，我都是2.6.0版本 | {
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"好的，我检查一下（github直接查看内容，很多图片都显示不出来，建议下载下来用typora看）"
] | 2022-04-12T01:31:04 | 2022-04-17T07:23:53 | 2022-04-17T07:23:53 | NONE | null | 你好，我在阅读学习https://github.com/datawhalechina/fun-rec/blob/master/docs/%E7%AC%AC%E4%B8%80%E7%AB%A0%20%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E5%9F%BA%E7%A1%80/1.1%20%E5%9F%BA%E7%A1%80%E6%8E%A8%E8%8D%90%E7%AE%97%E6%B3%95/1.1.2%20%E5%8D%8F%E5%90%8C%E8%BF%87%E6%BB%A4.md
发现有一些图片丢失问题。请问方便重新上传吗 | {
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