LightFM模型：内部的得分和Sigmoid函数
我有两个与LightFM模型有关的问题：
- 我阅读文章关于模型，我看到它使用了sigmoid f（。）函数。我还检查了图书馆的
- 我是否纠正了返回的分数是q_u * p_i + b_u + b_i（请参阅文章）？如果没有，我该如何计算自己的分数？它们来自哪里，为什么它们的幅度如此之高？我的分数大约从-100000到+100000。
upd1：我遵循评论并找到了以下功能：
cdef inline flt compute_prediction_from_repr(flt *user_repr,
flt *item_repr,
int no_components) nogil:
cdef int i
cdef flt result
# Biases
result = user_repr[no_components] + item_repr[no_components]
# Latent factor dot product
for i in range(no_components):
result += user_repr[i] * item_repr[i]
return result
似乎分数确实是上面的公式，但是如果有人也可以看一下，这将很有帮助 - 我对Cython
Upd2：Sigmoid使用 不太好仅适用于模型的逻辑变体。如果您尝试经过翘曲，则不会使用它。
I have two questions related to the LightFM model:
- I read the article about the model and I see that it uses sigmoid f(.)-function. I also checked library's Cython code and I see that the function is implemented there as well. However, the model is applicable to rank items in the rating setting (rating from 1 to 5). Why isn't sigmoid harming the ranking system? I mean it returns the value from 0 to 1, why the model still works for ratings?
- Am I correct that the scores which model returns is q_u * p_i + b_u + b_i (see the article)? If not, how can I calculate the scores myself? Where do they come from and why their magnitude is so high? I get the scores approximately from -100000 to +100000.
UPD1: I followed the comments and found out the following function:
cdef inline flt compute_prediction_from_repr(flt *user_repr,
flt *item_repr,
int no_components) nogil:
cdef int i
cdef flt result
# Biases
result = user_repr[no_components] + item_repr[no_components]
# Latent factor dot product
for i in range(no_components):
result += user_repr[i] * item_repr[i]
return result
It seems like the scores are indeed the formula above, but it would be helpful if someone could also have a look - I'm not very good with Cython
UPD2: sigmoid is used only for the logistic variant of the model. It's not used if you try WARP.
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该模型用于使用Sigmoid的评分，因为LightFM 二进制推荐问题。
对于1至5之间的评分，最高的
是使用A AUC得分表示模型性能的原因。
对于单个用户，AUC对应于随机选择的阳性项目的排名高于随机选择的负项目的概率。
就我而言，我应用了 Warp 损失，并使用Warp得分作为向用户在功能空间中接近物品的指标，以被用户喜欢。对于概率分数或评分预测，可以考虑其他复杂模型。
The model works for ratings using Sigmoid because LightFM binarizes the recommendation problem.
For ratings between 1 to 5 with 5 being the highest,
This is the reason model performance is indicated using a AUC score.
For an individual user, AUC corresponds to the probability that a randomly chosen positive item will be ranked higher than a randomly chosen negative item.
In my case I applied the WARP Loss and use WARP score as an indicator to closeness of the item to the user in feature space to being liked by the User. For a probabilistic score or ratings prediction other sophisticated models may be considered.