Re: How to recommend most similar users using Spark ML
There are also some Spark packages for finding approximate nearest
neighbors using locality sensitive hashing:
https://spark-packages.org/?q=tags%3Alsh
On Fri, Jul 15, 2016 at 7:45 AM nguyen duc Tuan <newvalu...@gmail.com>
wrote:
> Hi jeremycod,
> If you want to find top N nearest neighbors for all users using exact
> top-k algorithm for all users, I recommend using the same approach as as
> used in Mllib :
> https://github.com/apache/spark/blob/85d6b0db9f5bd425c36482ffcb1c3b9fd0fcdb31/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala#L272
>
> If the number of users is large, the exact topk algorithm can rather slow,
> try using approximate nearest neighbors algorithm. There's is a good
> benchmark of various libraries that can be found here:
> https://github.com/erikbern/ann-benchmarks
>
> 2016-07-15 10:36 GMT+07:00 jeremycod <zoran.jere...@gmail.com>:
>
>> Hi,
>>
>> I need to develop a service that will recommend user with other similar
>> users that he can connect to. For each user I have a data about user
>> preferences for specific items in the form:
>>
>> user, item, preference
>> 1,75, 0.89
>> 2,168, 0.478
>> 2,99, 0.321
>> 3,31, 0.012
>>
>> So far, I implemented approach using cosine similarity that compare one
>> user
>> features vector with other users:
>>
>> def cosineSimilarity(vec1: DoubleMatrix, vec2: DoubleMatrix): Double=
>> {
>> vec1.dot(vec2)/(vec1.norm2()*vec2.norm2())
>> }
>> def user2usersimilarity(userid:Integer, recNumber:Integer): Unit ={
>> val userFactor=model.userFeatures.lookup(userid).head
>> val userVector=new DoubleMatrix(userFactor)
>> val s1=cosineSimilarity(userVector,userVector)
>> val sims=model.userFeatures.map{case(id,factor)=>
>> val factorVector=new DoubleMatrix(factor)
>> val sim=cosineSimilarity(factorVector, userVector)
>> (id,sim)
>> }
>> val sortedSims=sims.top(recNumber+1)(Ordering.by[(Int, Double),Double]
>> {case(id, similarity)=>similarity})
>> println(sortedSims.slice(1,recNumber+1).mkString("\n"))
>> }
>>
>> This approach works fine with the MovieLens dataset in terms of quality of
>> recommendations. However, my concern is related to performance of such
>> algorithm. Since I have to generate recommendations for all users in the
>> system, with this approach I would compare each user with all other users
>> in
>> the system.
>>
>> I would appreciate if somebody could suggest how to limit comparison of
>> the
>> user to top N neighbors, or some other algorithm that would work better in
>> my use case.
>>
>> Thanks,
>> Zoran
>>
>>
>>
>>
>> --
>> View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/How-to-recommend-most-similar-users-using-Spark-ML-tp27342.html
>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>
>> -
>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>
>>
>
Re: How to recommend most similar users using Spark ML
Hi jeremycod,
If you want to find top N nearest neighbors for all users using exact top-k
algorithm for all users, I recommend using the same approach as as used in
Mllib :
https://github.com/apache/spark/blob/85d6b0db9f5bd425c36482ffcb1c3b9fd0fcdb31/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala#L272
If the number of users is large, the exact topk algorithm can rather slow,
try using approximate nearest neighbors algorithm. There's is a good
benchmark of various libraries that can be found here:
https://github.com/erikbern/ann-benchmarks
2016-07-15 10:36 GMT+07:00 jeremycod <zoran.jere...@gmail.com>:
> Hi,
>
> I need to develop a service that will recommend user with other similar
> users that he can connect to. For each user I have a data about user
> preferences for specific items in the form:
>
> user, item, preference
> 1,75, 0.89
> 2,168, 0.478
> 2,99, 0.321
> 3,31, 0.012
>
> So far, I implemented approach using cosine similarity that compare one
> user
> features vector with other users:
>
> def cosineSimilarity(vec1: DoubleMatrix, vec2: DoubleMatrix): Double=
> {
> vec1.dot(vec2)/(vec1.norm2()*vec2.norm2())
> }
> def user2usersimilarity(userid:Integer, recNumber:Integer): Unit ={
> val userFactor=model.userFeatures.lookup(userid).head
> val userVector=new DoubleMatrix(userFactor)
> val s1=cosineSimilarity(userVector,userVector)
> val sims=model.userFeatures.map{case(id,factor)=>
> val factorVector=new DoubleMatrix(factor)
> val sim=cosineSimilarity(factorVector, userVector)
> (id,sim)
> }
> val sortedSims=sims.top(recNumber+1)(Ordering.by[(Int, Double),Double]
> {case(id, similarity)=>similarity})
> println(sortedSims.slice(1,recNumber+1).mkString("\n"))
> }
>
> This approach works fine with the MovieLens dataset in terms of quality of
> recommendations. However, my concern is related to performance of such
> algorithm. Since I have to generate recommendations for all users in the
> system, with this approach I would compare each user with all other users
> in
> the system.
>
> I would appreciate if somebody could suggest how to limit comparison of the
> user to top N neighbors, or some other algorithm that would work better in
> my use case.
>
> Thanks,
> Zoran
>
>
>
>
> --
> View this message in context:
> http://apache-spark-user-list.1001560.n3.nabble.com/How-to-recommend-most-similar-users-using-Spark-ML-tp27342.html
> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>
> -
> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>
>
How to recommend most similar users using Spark ML
Hi,
I need to develop a service that will recommend user with other similar
users that he can connect to. For each user I have a data about user
preferences for specific items in the form:
user, item, preference
1,75, 0.89
2,168, 0.478
2,99, 0.321
3,31, 0.012
So far, I implemented approach using cosine similarity that compare one user
features vector with other users:
def cosineSimilarity(vec1: DoubleMatrix, vec2: DoubleMatrix): Double=
{
vec1.dot(vec2)/(vec1.norm2()*vec2.norm2())
}
def user2usersimilarity(userid:Integer, recNumber:Integer): Unit ={
val userFactor=model.userFeatures.lookup(userid).head
val userVector=new DoubleMatrix(userFactor)
val s1=cosineSimilarity(userVector,userVector)
val sims=model.userFeatures.map{case(id,factor)=>
val factorVector=new DoubleMatrix(factor)
val sim=cosineSimilarity(factorVector, userVector)
(id,sim)
}
val sortedSims=sims.top(recNumber+1)(Ordering.by[(Int, Double),Double]
{case(id, similarity)=>similarity})
println(sortedSims.slice(1,recNumber+1).mkString("\n"))
}
This approach works fine with the MovieLens dataset in terms of quality of
recommendations. However, my concern is related to performance of such
algorithm. Since I have to generate recommendations for all users in the
system, with this approach I would compare each user with all other users in
the system.
I would appreciate if somebody could suggest how to limit comparison of the
user to top N neighbors, or some other algorithm that would work better in
my use case.
Thanks,
Zoran
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/How-to-recommend-most-similar-users-using-Spark-ML-tp27342.html
Sent from the Apache Spark User List mailing list archive at Nabble.com.
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To unsubscribe e-mail: user-unsubscr...@spark.apache.org
