I am trying to learn Apache mahout, very new to this topic. I want to implement user-based recommender. For this, after exploring on the internet I have found some samples like below,
public static void main(String[] args) {
try {
int userId = 2;
DataModel model = new FileDataModel(new File("data/mydataset.csv"), ";");
UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
List<RecommendedItem> recommendations = recommender.recommend(userId, 3);
for (RecommendedItem recommendation : recommendations) {
logger.log(Level.INFO, "Item Id recommended : " + recommendation.getItemID() + " Ratings : "
+ recommendation.getValue() + " For UserId : " + userId);
}
} catch (Exception e) {
logger.log(Level.SEVERE, "Exception in main() ::", e);
}
I am using following dataset which contains userid, itemid, preference value respectively,
1,10,1.0
1,11,2.0
1,12,5.0
1,13,5.0
1,14,5.0
1,15,4.0
1,16,5.0
1,17,1.0
1,18,5.0
2,10,1.0
2,11,2.0
2,15,5.0
2,16,4.5
2,17,1.0
2,18,5.0
3,11,2.5
3,12,4.5
3,13,4.0
3,14,3.0
3,15,3.5
3,16,4.5
3,17,4.0
3,18,5.0
4,10,5.0
4,11,5.0
4,12,5.0
4,13,0.0
4,14,2.0
4,15,3.0
4,16,1.0
4,17,4.0
4,18,1.0
In this case, it works fine, but my main question is I have the different set of data which don't have preference values, which contains some data based on that I am thinking to compute preference values. Following is my new dataset,
userid itemid likes shares comments
1 4 1 20 3
2 6 18 20 12
3 12 10 2 20
4 7 0 20 13
5 9 0 2 1
6 5 5 3 2
7 3 9 7 0
8 1 15 0 0
My question is how can I compute preference value for a particular record based on some other columns such as likes, shares, comments etc. Is there anyway to compute this in mahout?