Does a country code secondary domain (such as have lower ranking than a ccTLD (such as .nl) with content specific for that country?

I know that search engines like Google value more country code top level domains (ccTLD) on content that is specific to residents living in said countries, that is, the website hosted at and whose content is specific for Dutch visitors has a higher rank than

And what about Does this example is negatively affected in contrast to

Return a ranking points of User follow week, month, year from multiple table with mySQL

Here is the structure of my tables:


|--------|------------| | id     | name       | |--------|------------| | 1      | Name1      | | 2      | Name2      | | 3      | Name3      | |--------|------------| 


|--------|------------|-------------|--------------------| | id     | content    | user_id     |   created_at       |  |--------|------------|-------------|--------------------| | 1      | Content1   |  1          |2020-01-17 14:03:31 | | 2      | Content2   |  1          |2020-01-17 16:18:23 | | 3      | Content3   |  2          |2020-01-17 16:29:13 |  |--------|------------|-------------|--------------------| 


|--------|------------|-------------|----------|---------------------| | id     | comment    | user_id     | post_id  |   created_at        | |--------|------------|-------------|----------|---------------------| | 1      | Comment1   |  1          |   1      | 2020-01-20 18:29:19 | | 2      | Comment2   |  1          |   1      | 2020-01-22 17:25:49 | | 3      | Comment3   |  2          |   2      | 2020-01-28 11:37:59 |  |--------|------------|-------------|----------|---------------------| 


|--------|-------------|----------|-----------------------| | id     |  user_id    | post_id  |    created_at         | |--------|-------------|----------|-----------------------| | 1      |   1         |   1      | 2020-01-20 15:08:55.0 | | 2      |   1         |   2      | 2020-01-20 15:13:29   | | 3      |   2         |   2      | 2020-01-20 15:13:32   | |--------|-------------|----------|-----------------------| 

I want to find the top score of 10 users by week, month, the year following to formula:

A user creates 1 post will have 10 points. A user creates 1 comment in a post that will have 5 points. A user votes in a post that will have 2 points. Could anyone help me with this, please!

Thank you so much.

A survey on ranking keyword search results

To rank the keyword search results, I’m trying to crack the way the Airbnb algorithm or similar ones work. I’m not asking which features they are using since those are different depending on the business needs. What I’m asking is where can I find a paper/survey/book to see what are the various relevancy metrics in addition to TF-IDF and PageRank? and how can I merge various metrics/algorithms into a single algorithm to sort the search results?

Input: a couple of keywords Output: a ranked list of options relevant to the input keywords

Anything from links to surveys/books/algorithms/open-source software would work.


Calculating match % and ranking according to that

I’m creating a website like where users will answer some yes/no questions set by me, up to them how many of those questions they want to answer. After a user submits his answer(s), he will be shown top 5 matches along with their match percentages. If two users have 10 common questions and their answers match for 8 of those questions then their match % will be 80%.

I can make this but my concern is about efficiency. A way of making this: If a user wants to see his top matches then match % (or match ratio) will be calculated for him vs every other user in the system. This will be stored in a temporary array. Array is sorted. Top 5 matches from the array are displayed.

Any less resource intensive way to calculate and show top matches?

Why am I ranking on last page in SERP for every keyword? [closed]

I couldn’t find this question anywhere. Everyone is talking about ranking and how to rank on top in SERPs. I used to rank on top 2 or 3 pages a lot but Now I don’t rank anymore. My content is authoritative and the site is also well optimized. Any suggestions?


Keyboard- Vivo y5s full specification, Location – India

Ranking function approximation

I have a matrix function, which roughly looks like this: $ $ Y_{i,j,k} = f(coef, A, B) = coef[i] * A_{i,j} * B_{i,k} $ $

def fn(coef, A, B):     return np.einsum("i,ij,ik->ijk", coef, A, B) 

Given, say, following values for coef, A, B:

coef = [1, 4, 16] A = [     [.11, .12],     [.91, .52],     [.31, .32] ] B = [     [.11, .12, .13],     [.91, .72, .63],     [.31, .52, .73] ] 

3d matrix produced by function fn can be visualized like this (I’m just stacking together two slices horizontally, to make it possible to visualize a matrix in 2d):

heatmap(     np.hstack((         y[:,0,:],         y[:,1,:]     )) ) 

enter image description here

Given a target (constant) 3d matrix, I need to find values for input parameters A and B such, that they restore element ranks along axis=0.

enter image description here

I was trying to solve this problem minimizing following objective function, using SLSQP implementation provided by scipy.optimize package:

$ C=\sum_{j,k} |R_{jk}-\hat R_{jk}|$ , where:

$ R$ – given target constant matrix ranks
$ \hat R$ – actual matrix ranks
$ R = argsort(Y) $

def objective_fn(coef, A, B, expected_matrix):     expected_ranks = expected_matrix.argsort(axis=0)     actual_ranks = np.einsum("i,ij,ik->ijk", coef, A, B)     diff = abs(expected_ranks - actual_ranks)     return diff.sum() 

It sort of works.

enter image description here

However, results don’t always match expectations. It seems to me, that since objective ranking function is not differentiable, classic optimization using SLSQP may not be an optimal approach here.


  1. Are there any better ways to solve a problem like this?

  2. Is it possible to use matrix decomposition/factorization here so that result factor matrices have specified dimensionality?

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