To recommend users to users, you need some kind of user-user interaction data. You are right, you don't have that directly. But at first you described this as merely finding similar users. For that, you can use your data. You don't need a Recommender even. You just need any implementation of UserSimilarity. This can help you find which users are most similar -- just loop over all of them and keep the top N. (There are classes like TopN that let you do this easily.)
It's the same for items. DataModel has methods to get all user and item IDs. You can't use a JDBC data source directly unless you have very little data. It's way too slow. You will need to load it in memory with ReloadFromJDBCDataModel. VM settings are irrelevant in this regard. On Thu, Jun 14, 2012 at 2:24 PM, k4200 <[email protected]> wrote: > Hi, > > I bought Mahout in Action several days ago and am now trying Mahout > out. I've also read two books about collective intelligence, so I > think I have some basic knowledge. > > Before going to my questions, here's my use case: > * I'm developing a web site that has > - users, items and preferences in MySQL > - item pages that both logged-in and non-logged-in users can view > * I'd like to > - recommend items for logged-in users (#1) > - recommend similar users based on preferences for logged-in users (#2) > - show similar items on every item page (#3) > > #1 is the typical scenario that the book and other web pages cover, so > it shouldn't be a problem, and actually I wrote code that seems > working more or less shown below. Though, I have a question regarding > performance, which I'll write later. > > MySQLJDBCDataModel dataModel = new MySQLJDBCDataModel (dataSource, ....); > ItemSimilarity itemSimilarity = new > PearsonCorrelationSimilarity(dataModel); > Recommender recommender = new GenericItemBasedRecommender(dataModel, > itemSimilarity); > // Then, for each user, get recommendations and store them in DB > > > My first question is how to implement #2. Chapter 5 of the book seems > a bit similar, but the difference is that our users don't rate other > users (or profiles). I have no clue how to achieve this using Mahout, > so any hints/suggestions would be appreciated. > > The second question is about #3. Each item page needs to show similar > items, which I believe is a typical use case for many web sites. The > code above calculates item similarity so I'm thinking of storing the > data to DB. It seems like I need to call allSimilarItemIDs for each > item ID, but is there any way to get all the item IDs? Of course, I > could execute a query via JDBC, which would be a bit of hassle. > > The last question is regarding performance. I set the JDBC driver > options according to the javadoc shown below. > > https://builds.apache.org/job/Mahout-Quality/javadoc/org/apache/mahout/cf/taste/impl/model/jdbc/MySQLJDBCDataModel.html > > I use test data of several thousands of preferences, so the > calculations should be fast, but it took more than 10 minutes. What > should I do to speed it up? > > Here's the code. > MysqlDataSource dataSource = new MysqlDataSource(); > dataSource.setServerName("hostname"); > dataSource.setUser("user"); > dataSource.setPassword("pass"); > dataSource.setDatabaseName("mydb"); > > dataSource.setCachePreparedStatements(true); > dataSource.setCachePrepStmts(true); > dataSource.setCacheResultSetMetadata(true); > dataSource.setAlwaysSendSetIsolation(false); > dataSource.setElideSetAutoCommits(true); > > I follow the VM settings on a page on the Mahout site (or somewhere else). > -server -Xms1024m -Xmx1024m -da -dsa -XX:NewRatio=9 -XX:+UseParallelGC > -XX:+UseParallelOldGC -XX:-DisableExplicitGC > > Thank you, > Kaz >
