Use of negative correlation coefficient in pearson correlation algorithm for recommender systems
$begingroup$
I'm new in recommender systems and I try to find similar users of a base users for user-based collaborative filtering.
When I calculated the similarity score now between two users (based on there ratings with pearson algorithm [or resnick's weighted pearson algorithm]) I get a similarity score from -1 to 1.
Is it a good idea to normalize this values to 0 to 1 (-1 would become 0 and 1 would be 1) to make it comparable to other algorithms?
In fact I try to build recommendations and with a negative similarity score of a user the calculated/predicted rating could be negative as well which make no sense.
Should I normalize/scale "-1 to 1" to "0 to 1" or cut off all users with similarity score below 0?
(maybe the question also could be: "Which users should be taken as mentor to recommend new items on a similarity score from -1 to 1? Or should I take the top n users with highest similarity score?")
recommender-system
asked Jun 25 '17 at 22:56
5im5im
112
$endgroup$
bumped to the homepage by Community♦ 9 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I'm new in recommender systems and I try to find similar users of a base users for user-based collaborative filtering.
When I calculated the similarity score now between two users (based on there ratings with pearson algorithm [or resnick's weighted pearson algorithm]) I get a similarity score from -1 to 1.
Is it a good idea to normalize this values to 0 to 1 (-1 would become 0 and 1 would be 1) to make it comparable to other algorithms?
In fact I try to build recommendations and with a negative similarity score of a user the calculated/predicted rating could be negative as well which make no sense.
Should I normalize/scale "-1 to 1" to "0 to 1" or cut off all users with similarity score below 0?
(maybe the question also could be: "Which users should be taken as mentor to recommend new items on a similarity score from -1 to 1? Or should I take the top n users with highest similarity score?")
recommender-system
asked Jun 25 '17 at 22:56
5im5im
112
$endgroup$
bumped to the homepage by Community♦ 9 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I'm new in recommender systems and I try to find similar users of a base users for user-based collaborative filtering.
When I calculated the similarity score now between two users (based on there ratings with pearson algorithm [or resnick's weighted pearson algorithm]) I get a similarity score from -1 to 1.
Is it a good idea to normalize this values to 0 to 1 (-1 would become 0 and 1 would be 1) to make it comparable to other algorithms?
In fact I try to build recommendations and with a negative similarity score of a user the calculated/predicted rating could be negative as well which make no sense.
Should I normalize/scale "-1 to 1" to "0 to 1" or cut off all users with similarity score below 0?
(maybe the question also could be: "Which users should be taken as mentor to recommend new items on a similarity score from -1 to 1? Or should I take the top n users with highest similarity score?")
recommender-system
asked Jun 25 '17 at 22:56
5im5im
112
$endgroup$
I'm new in recommender systems and I try to find similar users of a base users for user-based collaborative filtering.
When I calculated the similarity score now between two users (based on there ratings with pearson algorithm [or resnick's weighted pearson algorithm]) I get a similarity score from -1 to 1.
Is it a good idea to normalize this values to 0 to 1 (-1 would become 0 and 1 would be 1) to make it comparable to other algorithms?
In fact I try to build recommendations and with a negative similarity score of a user the calculated/predicted rating could be negative as well which make no sense.
Should I normalize/scale "-1 to 1" to "0 to 1" or cut off all users with similarity score below 0?
(maybe the question also could be: "Which users should be taken as mentor to recommend new items on a similarity score from -1 to 1? Or should I take the top n users with highest similarity score?")
recommender-system
recommender-system
asked Jun 25 '17 at 22:56
5im5im
112
asked Jun 25 '17 at 22:56
5im5im
112
asked Jun 25 '17 at 22:56
5im5im
112
asked Jun 25 '17 at 22:56
5im5im
112
asked Jun 25 '17 at 22:56
5im5im
112
112
bumped to the homepage by Community♦ 9 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 9 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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$begingroup$
One thing you can do is to separate the contributions of:
- a) who have a positive correlation with you
- b) who have a negative correlation with you
Then you can:
- Predict the rating using only users in a) $to$ those will have positive correlation, this positive weights. Call this $hat{r}_a$
- Predict the rating using only users in b) $to$ in this case, consider the weights as positive (even in they are negative correlation). Call this $hat{r}_b$
- The final predicted rating is $hat{r} = frac{hat{r}_a - hat{r}_b}{2}$
answered Jan 10 at 19:23
Mirko MazzoleniMirko Mazzoleni
11
$endgroup$
add a comment |
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1 Answer
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$begingroup$
One thing you can do is to separate the contributions of:
- a) who have a positive correlation with you
- b) who have a negative correlation with you
Then you can:
- Predict the rating using only users in a) $to$ those will have positive correlation, this positive weights. Call this $hat{r}_a$
- Predict the rating using only users in b) $to$ in this case, consider the weights as positive (even in they are negative correlation). Call this $hat{r}_b$
- The final predicted rating is $hat{r} = frac{hat{r}_a - hat{r}_b}{2}$
answered Jan 10 at 19:23
Mirko MazzoleniMirko Mazzoleni
11
$endgroup$
add a comment |
$begingroup$
One thing you can do is to separate the contributions of:
- a) who have a positive correlation with you
- b) who have a negative correlation with you
Then you can:
- Predict the rating using only users in a) $to$ those will have positive correlation, this positive weights. Call this $hat{r}_a$
- Predict the rating using only users in b) $to$ in this case, consider the weights as positive (even in they are negative correlation). Call this $hat{r}_b$
- The final predicted rating is $hat{r} = frac{hat{r}_a - hat{r}_b}{2}$
answered Jan 10 at 19:23
Mirko MazzoleniMirko Mazzoleni
11
$endgroup$
add a comment |
$begingroup$
One thing you can do is to separate the contributions of:
- a) who have a positive correlation with you
- b) who have a negative correlation with you
Then you can:
- Predict the rating using only users in a) $to$ those will have positive correlation, this positive weights. Call this $hat{r}_a$
- Predict the rating using only users in b) $to$ in this case, consider the weights as positive (even in they are negative correlation). Call this $hat{r}_b$
- The final predicted rating is $hat{r} = frac{hat{r}_a - hat{r}_b}{2}$
answered Jan 10 at 19:23
Mirko MazzoleniMirko Mazzoleni
11
$endgroup$
One thing you can do is to separate the contributions of:
- a) who have a positive correlation with you
- b) who have a negative correlation with you
Then you can:
- Predict the rating using only users in a) $to$ those will have positive correlation, this positive weights. Call this $hat{r}_a$
- Predict the rating using only users in b) $to$ in this case, consider the weights as positive (even in they are negative correlation). Call this $hat{r}_b$
- The final predicted rating is $hat{r} = frac{hat{r}_a - hat{r}_b}{2}$
answered Jan 10 at 19:23
Mirko MazzoleniMirko Mazzoleni
11
edited Jan 11 at 19:28
answered Jan 10 at 19:23
Mirko MazzoleniMirko Mazzoleni
11
answered Jan 10 at 19:23
Mirko MazzoleniMirko Mazzoleni
11
answered Jan 10 at 19:23
Mirko MazzoleniMirko Mazzoleni
11
11
add a comment |
add a comment |
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