On 05/14/2012 05:09 PM, Sean Owen wrote:
Yes, you don't want to use the database directly. A relational
database will never be fast enough.
You want to use RefreshFromJDBCDataModel to load the data into memory
periodically. This is really what I was referring to.
On Mon, May 14, 2012 at 1:49 PM, Nikolaos Romanos Katsipoulakis
<[email protected]> wrote:
Thank you for your immediate response. Also, I have one more question. When
I use data from a Mysql database, my recommender is very slow, about 2
minutes for a 1 million records dataset. The code of the recommender is
presented below:
this.dataSource = new MysqlDataSource();
this.dataSource.setServerName(serverName);
this.dataSource.setUser(user);
this.dataSource.setPassword(pass);
this.dataSource.setDatabaseName(dbName);
this.dataModel = new MySQLJDBCDataModel(this.dataSource, this.tableName,
this.userColumn, this.itemColumn, this.prefColumn,
this.timeStampColumn);
this.similarity = new EuclideanDistanceSimilarity(this.dataModel);
this.neighborhood = new NearestNUserNeighborhood(neighborhoodSize,
this.similarity, this.dataModel);
this.recommender = new GenericUserBasedRecommender(this.dataModel,
this.neighborhood, this.similarity);
List<RecommendedItem> recommendations = null;
recommendations = recommender.recommend(user, NumOfRecommendations);
Why is it so slow? I should mention that i also used
MysqlConnectionPoolDataSource() and the performance remains the same.
Ok. Also, I have one more question. While I was reading the
GenericItemSimilarity javadoc, I came across this:
A "generic" |GenericItemSimilarity.ItemItemSimilarity|
<https://builds.apache.org/job/Mahout-Quality/javadoc/org/apache/mahout/cf/taste/impl/similarity/GenericItemSimilarity.ItemItemSimilarity.html>
which takes a static list of precomputed item similarities and bases its
responses on that alone. The values may have been precomputed offline by
another process, *stored in a file*, and then read and fed into an
instance of this class.
How is this done? This is what I actually needed from the beginning.
Thank you.
