public static void itemItemRecommendations(String path, String file) {
		File f = new File(path, file);
	    try {
			DataModel model = new FileDataModel(f);
			model.refresh(null);
		    ItemSimilarity itemSimilarity = new LogLikelihoodSimilarity(model);
		    ItemBasedRecommender itemRecommender = new GenericItemBasedRecommender(model, itemSimilarity);
		    for ( Item i : model.getItems() )
			    for ( RecommendedItem j : itemRecommender.mostSimilarItems(i.getID(), 50) )
			    	if ( j.getValue() >= 0.7 )
			    		System.out.println(i.getID() + "\t" + j.getItem().getID() + "\t" + String.format("%.3f", j.getValue()));
		} catch (FileNotFoundException e) {
			// TODO Auto-generated catch block
			e.printStackTrace();
		} catch (TasteException e) {
			// TODO Auto-generated catch block
			e.printStackTrace();
		}
	}

This outputs item1 –recommends–>item2 pairs with a weight. I’m taking this and putting it into a solr document so I can display related item2s alongside item1 when it’s viewed.

Input data are comma-delimited tuples like so:

1fe7401b81eed49353d0cbeba5383848,5212,0.6
3c1832954a6e8781836fed670bb37b24,5212,1
70273e4c7c77700ee97acb8d0306c405,5213,0.8
1f057ccde135acbc881008bbf466e7e1,5213,1
51d44c7baca65ad39d11ba87bf2d438b,5213,1
adc924559b37114cd97d1f5cf7c71419,5213,1
78e254b4a11e61d76ff63cea02de4de8,5213,1
5c373ec7d9ad4a6f392c291d8ccba5ce,5213,0.2
fab8537564094fa8885f6214e6b682e1,5213,1
127f46aabcdbc2d2d04da8398a996c75,5213,1

Works great. Thanks Sean.

Anyway, I posted an adapter class to Apache JIRA on Friday for Mahout / HBase integration that allows HTables to effectively be manipulated as sparse vectors (read through RowResult, write through BatchUpdate) in Mahout using the Vector interface. Check it out, MAHOUT-78.

Looks like Mahout needs to generate some HTML for their javadoc.