Closed huyenntn closed 5 years ago
SLIM based approaches are top-N recommendation algorithms. They cannot be evaluated by MAE or RMSE
On Sun, Dec 17, 2017 at 02:48 Huyenntn notifications@github.com wrote:
I configured tem.ranking=off -topN 10 but results of SLIM was still Pre, Rec,... How can I get MAE and RMSE values?
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Can you tell me what the config "-minlenu 2 -minleni 2" mean in usersplitting, itemsplitting and uisplitting algorithms. Thankyou very much!
Let's take item splitting for example, ratings canbe split into two sets: weekend and weekday. Next, we are going to use statistics to evaluate whether there is significant different in ratings in these two contexts
The minlenu defines the minimal length of the set. If the size is too small, the result by statistic is not reliable
In other words, if you increase the minimal length, the user or item will be split when the two sets meet the length requirement and pass the significance test
On Thu, Dec 21, 2017 at 15:23 Huyenntn notifications@github.com wrote:
Can you tell me what the config "-minlenu 2 -minleni 2" mean in usersplitting, itemsplitting and uisplitting algorithms. Thankyou very much!
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Thankyou and sorry to bother. I have another question. When I used DePaulMovie dataset with UserKNN. It's is a traditional recommender system. Why the context still existed in the result. And the second question is what does "-lc1 -lc2 and reg.lambda=0.0001 -c 0.001" in file config mean?
If u run userKNN, it will make predictions without considering context
For lc parameters, you need to read the paper or source codes
On Thu, Dec 21, 2017 at 20:06 Huyenntn notifications@github.com wrote:
Thankyou and sorry to bother. I have another question. When I used DePaulMovie dataset with UserKNN. It's is a traditional recommender system. But but the context still existed in the result. And the second question is what does "-lc1 -lc2 and reg.lambda=0.0001 -c 0.001" in file config mean?
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For example with predictions in UserKNN-top-10-items fold [1].txt: 1003, companion:alone;location:cinema;time:weekday: (tt0315733, 2.4684935), (tt0109830*, 2.2845337), (tt0111161, 2.2583814), (tt1375666, 2.0370011), (tt0441773, 1.9667363), (tt0268380, 1.9145375), (tt0266543, 1.9062306), (tt1478338, 1.8835531), (tt0125439, 1.8686336), (tt2096673, 1.8268628). It contains the context
And if it doesn't use the context, a vector (userid, itemid) may be duplicated. How did you solve it?
We use average rating for user item pair
On Thu, Dec 21, 2017 at 21:17 Huyenntn notifications@github.com wrote:
And if it doesn't use the context, a vector (userid, itemid) may be duplicated. How did you solve it?
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Can you give me the best config content for cslim_mcs algorithm with movie depaul data. I try it but evaluation value too bad.
I don't have that at hand. For SLIM based algorithms, they are sensitive to initializations and running parameters. You may need to carefully to tune up the parameters
On Sat, Dec 23, 2017 at 11:17 Huyenntn notifications@github.com wrote:
Can you give me the best config content for cslim_mcs algorithm with movie depaul data. I try it but evaluation value too bad.
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Hello Yong Zheng. You still haven't answered my question. UserKNN make predictions without considering context. Why the context still existed in the result? Example of the result: https://i.imgur.com/kiLX9eg.png
The outputs include contexts for each line. But as I said previously, userknn will be run without considering contexts
On Sat, Dec 30, 2017 at 06:12 Huyenntn notifications@github.com wrote:
Hello Yong Zheng. You still haven't answered my question. UserKNN make predictions without considering context. Why the context still existed in the result? Example of the result: https://i.imgur.com/kiLX9eg.png
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I configured tem.ranking=off -topN 10 but results of SLIM was still Pre, Rec,... How can I get MAE and RMSE values?