collaborative-filtering | Recommendation engine based on collaborative | Recommender System library
kandi X-RAY | collaborative-filtering Summary
kandi X-RAY | collaborative-filtering Summary
This API is a lightweight implementation of collaborative filtering for Node.js. The only dependency is Math.js. Currently it supports generating recommendations with Jaccard similarity.
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Community Discussions
Trending Discussions on collaborative-filtering
QUESTION
I am trying to re-write this code, which was written a while ago.
It has multiple chuncks, hence, I separated it into smaller pieces and re-write step by step. For instance, converting .ix to iloc, etc.
This chunk gives me an error:
...ANSWER
Answered 2020-Nov-11 at 03:38I'm not exactly sure what that piece of code is supposed to do (though it seems like there may be a more efficient way of doing it). Also, it looks like you are referencing j outside the j loop.
However, the specific error you're getting is mostly likely related to:
QUESTION
I've been working with dataset Movielens (20 million records) and have been using collaborative filtering in Spark MLlib.
My environment is Ubuntu 14.4 on VirtualBox. I have one master node and 2 slave nodes. I used the released Apache Hadoop, Apache Spark, Scala, sbt. The code is written in Scala.
How to distribute the code and the dataset onto worker nodes?
...ANSWER
Answered 2019-Jun-29 at 17:321 - Your dataset is best placed into a distributed file system - Hadoop HDFS, S3, etc.
2 - Code is distributed via the spark-submit script, as described here https://spark.apache.org/docs/2.4.3/submitting-applications.html
QUESTION
I am praticing this apache spark machine learning example to make a recommendation system for our users based on ratings they gave and I got this data which is top 10 recommendation product for a userId 31511 ( check out the last code of example above)
...ANSWER
Answered 2018-May-11 at 12:59Just explode and select
QUESTION
I am working through the example in the blog to understand collaborative filter method used in recommendation system.I came across cosine similarity expressed as
In python using numpy its written as
...ANSWER
Answered 2018-Apr-26 at 11:39The ratings are stored in the numpy matrix ratings, where rows correspond to users (index u), while columns correspond to items (index i). Since you want to calculate sim(u, u'), i.e., the similarity between users, let's assume below that kind = 'user'.
Now, let's have a look first on r_{ui}r_{u'i} without the square-root scaling factors. This expression is summed over i which can be interpreted as matrix multiplication of r with the transpose of r, i.e.:
QUESTION
I wish to transform a Collaborative Filtering with Python through Cosine Similarity to Adjusted Cosine Similarity.
The cosine similarity based implementation looks like this:
...ANSWER
Answered 2017-Mar-21 at 08:29Here's a NumPy based solution to your problem.
First we store rating data into an array:
QUESTION
I am trying to run ALS of PySpark. I copied & pasted the sample code provided in the link. However, the error java.lang.IllegalArgumentException occurs at the line:
model = ALS.train(ratings, rank, numIterations)
May I ask what possible problems do I need to investigate here?
My Spark version is 2.2.1, my Java version is 9.0.4. However, I am not sure if Spark is using the right version of Java although I set the environment path and the command "java -version" does return "9.0.4".
Error:
...Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.api.python.SerDe.pythonToJava. : java.lang.IllegalArgumentException at org.apache.xbean.asm5.ClassReader.(Unknown Source) at org.apache.xbean.asm5.ClassReader.(Unknown Source) at org.apache.xbean.asm5.ClassReader.(Unknown Source) at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:46) at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:443) at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:426) at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733) at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103) at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103) at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230) at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40) at scala.collection.mutable.HashMap$$anon$1.foreach(HashMap.scala:103) at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732) at org.apache.spark.util.FieldAccessFinder$$anon$3.visitMethodInsn(ClosureCleaner.scala:426) at org.apache.xbean.asm5.ClassReader.a(Unknown Source) at org.apache.xbean.asm5.ClassReader.b(Unknown Source) at org.apache.xbean.asm5.ClassReader.accept(Unknown Source) at org.apache.xbean.asm5.ClassReader.accept(Unknown Source) at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:257) at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:256) at scala.collection.immutable.List.foreach(List.scala:381) at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:256) at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:156) at org.apache.spark.SparkContext.clean(SparkContext.scala:2294) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:794) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:793) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:362) at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:793) at org.apache.spark.mllib.api.python.SerDeBase.pythonToJava(PythonMLLibAPI.scala:1349) at org.apache.spark.mllib.api.python.SerDe.pythonToJava(PythonMLLibAPI.scala) at jdk.internal.reflect.GeneratedMethodAccessor76.invoke(Unknown Source) at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.base/java.lang.reflect.Method.invoke(Method.java:564) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:280) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:214) at java.base/java.lang.Thread.run(Thread.java:844)
ANSWER
Answered 2018-Feb-10 at 11:01I have figured out the problem. Spark 2.2.1 does not go with Java 9.0.4. Maybe I have misunderstood the part "Java 8+" of the tutorial.
If anyone meets the same error as me, just go for Java 1.8.0!
QUESTION
I am trying to write a simple vanilla collaborative filtering application, running on Google Cloud Dataproc. The Data is located in BigQuery. I have implemented this according to this tutorial: https://cloud.google.com/dataproc/docs/tutorials/bigquery-sparkml
Now the problem is that when running this (slightly modified) example I get an IllegalStateException. More specifically here is the stacktrace:
...ANSWER
Answered 2017-Oct-06 at 15:55This is a bug in BigQuery (that it returns the output file count statistics that does not include the zero-record file). The fix for this issue has been submitted, and its rollout will complete in about a week.
In the meantime, a workaround of the issue is maybe set the flag "mapred.bq.input.sharded.export.enable" (a.k.a. ENABLE_SHARDED_EXPORT_KEY) to false in your hadoop config when configuring your DataProc job.
UPDATE:
As of today Oct 6 2017, the fix is now 100% rolled out on BigQuery.
QUESTION
I am working in recommendation system. I have followed this to make user by item matrix. However, I faced an error IndexError: index 8928358160 is out of bounds for axis 0 with size 5
The following below is example of datasets.
...ANSWER
Answered 2017-Jun-20 at 06:41Why dont you convert the values to string? Eventhough it's as integer, the computer might take it as a scientific value and thus becoming a float value.
Try this:
Converting the cust_id and item_number into characters from float value:QUESTION
Does the Spark MLlib implementation of alternating least squares (http://spark.apache.org/docs/latest/mllib-collaborative-filtering.html) require that all zero entries for the training set (user-product combinations where the user has no history of interacting with the product) are manually created with a rating of 0, or will the algorithm automatically imply that all missing combinations have a zero rating?
...ANSWER
Answered 2017-Jun-10 at 15:19The training set can be sparse, and in fact, should be -- otherwise you'll pay a (possibly severe) performance penalty. See this discussion on the spark users mailing list for more information.
QUESTION
I'm working through an early version of Spark (alpha-0.1) to understand how it started and how it has evolved. I'm also trying to educate myself on how Alternating Least Squares works.
I'm looking through the SparkALS example and see the following variables:
...ANSWER
Answered 2017-Mar-18 at 08:52Ah, the Spark API docs refer to rank as the number of features:
rank - number of features to use
So I guess the terminology number of features and number of latent factors is interchangeable.
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