Re: Newbie question
+ Mahout user Sent from my iPhone On Mar 8, 2014, at 10:42 AM, Mahmood Naderan nt_mahm...@yahoo.commailto:nt_mahm...@yahoo.com wrote: Hi Maybe this is a newbie question but I want to know does Hadoop/Mahout use pthread models? Regards, Mahmood
Newbie Question
I have successfully run the Breiman example from https://cwiki.apache.org/confluence/display/MAHOUT/Breiman+Example How do I view the tree? Do I need to write a program that instantiates ForestVisualizer.java? Is there another program for visualizing the results of Mahout output? Many Thanks, Robby font size=2 The information transmitted is intended solely for the individual or entity to which it is addressed and may contain confidential and/or privileged material. Any review, retransmission, dissemination or other use of or taking action in reliance upon this information by persons or entities other than the intended recipient is prohibited. If you have received this email in error please contact the sender and delete the material from your computer (s). All incoming and outgoing e-mail is reviewed and archived by James River Capital Corp. and may be produced at the request of regulators or in connection with civil litigation. James River Capital Corp. accepts no liability for any errors or omissions arising as a result of transmission.brbr This document is neither an offer to sell nor a solicitation of an offer to buy any fund, product, security or other investment. An offer of investment can be made only via the approved offering documents, which contains important information concerning risk factors and other material aspects of the investment and must be read carefully before investing. Any person investing must be able to bear the risks involved and must meet the relevant suitability requirements. Some or all alternative investment programs may not be suitable for certain investors. No assurance can be given that the investment objectives of the investment will be achieved. Any historical price or value is as of the date indicated. Information is provided as of the date of this material only and are subject to change without notice. BPAST RESULTS ARE NOT NECESSARILY INDICATIVE OF FUTURE PERFORMANCE./bbrbr font size=2
Re: Newbie question on modeling a Recommender using Mahout when the matrix is sparse
Well there are only 7 products in the universe! If you ask for 10 recommendations, you will always get all unrated items back in the recommendations. That's always true unless the algorithm can't actually establish a value for some items. What result were you expecting, less than 10 recs? less than 7? On Thu, Sep 13, 2012 at 6:55 AM, Gokul Pillai gokoolt...@gmail.com wrote: I am trying out Mahout to come up with product recommendations for users based on data that show what products they use today. The data is not web-scale, just about 300,000 users and 7 products. Few comments about the data here: 1. Since users either have or not have a particular product, the value in the matrix is either 1 or 0 for all the columns (rows being the userids) 2. All the users have one basic product, so I discounted this from the data-model passed to the Mahout recommender since I assume that if everyone has the same product, its effect on the recommendations are trivial. 3. The matrix itself is sparse, the total counts of users having each product is : A=31847, 54754,1897 |23154 |2201 |2766 |33585 Steps followed: 1. Created a data-source from the user-product table in the database File ratingsFile = new File(datasets/products.csv); DataModel model = new FileDataModel(ratingsFile); 2. Created a recommender on this data CachingRecommender recommender = new CachingRecommender(new SlopeOneRecommender(model)); 3. Loop through all users and get the top ten recommendations: ListRecommendedItem recommendations = recommender.recommend(userId, 10); Issue faced: The problem I am facing is that the recommendations that come out are way too simple - meaning that all that it seems like what is being recommended is if a user does not have product A, then recommend it, if they dont have product B, then recommend it and so on. Basically a simple inverse of their ownership status. Obviously, I am not doing something right here. How can I do the modeling better to get the right recommendations. Or is it that my dataset (30 users times 7 products) is too small for Mahout to work with? Look forward to your comments. Thanks.
Re: Newbie question on modeling a Recommender using Mahout when the matrix is sparse
Very true, good catch. I think I was interpreting the results the wrong way. I expect only the top 5, so I changed the parameter to 5 instead of 10 and the results are as expected now. Thanks. On Wed, Sep 12, 2012 at 11:36 PM, Sean Owen sro...@gmail.com wrote: Well there are only 7 products in the universe! If you ask for 10 recommendations, you will always get all unrated items back in the recommendations. That's always true unless the algorithm can't actually establish a value for some items. What result were you expecting, less than 10 recs? less than 7? On Thu, Sep 13, 2012 at 6:55 AM, Gokul Pillai gokoolt...@gmail.com wrote: I am trying out Mahout to come up with product recommendations for users based on data that show what products they use today. The data is not web-scale, just about 300,000 users and 7 products. Few comments about the data here: 1. Since users either have or not have a particular product, the value in the matrix is either 1 or 0 for all the columns (rows being the userids) 2. All the users have one basic product, so I discounted this from the data-model passed to the Mahout recommender since I assume that if everyone has the same product, its effect on the recommendations are trivial. 3. The matrix itself is sparse, the total counts of users having each product is : A=31847, 54754,1897 |23154 |2201 |2766 |33585 Steps followed: 1. Created a data-source from the user-product table in the database File ratingsFile = new File(datasets/products.csv); DataModel model = new FileDataModel(ratingsFile); 2. Created a recommender on this data CachingRecommender recommender = new CachingRecommender(new SlopeOneRecommender(model)); 3. Loop through all users and get the top ten recommendations: ListRecommendedItem recommendations = recommender.recommend(userId, 10); Issue faced: The problem I am facing is that the recommendations that come out are way too simple - meaning that all that it seems like what is being recommended is if a user does not have product A, then recommend it, if they dont have product B, then recommend it and so on. Basically a simple inverse of their ownership status. Obviously, I am not doing something right here. How can I do the modeling better to get the right recommendations. Or is it that my dataset (30 users times 7 products) is too small for Mahout to work with? Look forward to your comments. Thanks.
Newbie question on modeling a Recommender using Mahout when the matrix is sparse
I am trying out Mahout to come up with product recommendations for users based on data that show what products they use today. The data is not web-scale, just about 300,000 users and 7 products. Few comments about the data here: 1. Since users either have or not have a particular product, the value in the matrix is either 1 or 0 for all the columns (rows being the userids) 2. All the users have one basic product, so I discounted this from the data-model passed to the Mahout recommender since I assume that if everyone has the same product, its effect on the recommendations are trivial. 3. The matrix itself is sparse, the total counts of users having each product is : A=31847, 54754,1897 |23154 |2201 |2766 |33585 Steps followed: 1. Created a data-source from the user-product table in the database File ratingsFile = new File(datasets/products.csv); DataModel model = new FileDataModel(ratingsFile); 2. Created a recommender on this data CachingRecommender recommender = new CachingRecommender(new SlopeOneRecommender(model)); 3. Loop through all users and get the top ten recommendations: ListRecommendedItem recommendations = recommender.recommend(userId, 10); Issue faced: The problem I am facing is that the recommendations that come out are way too simple - meaning that all that it seems like what is being recommended is if a user does not have product A, then recommend it, if they dont have product B, then recommend it and so on. Basically a simple inverse of their ownership status. Obviously, I am not doing something right here. How can I do the modeling better to get the right recommendations. Or is it that my dataset (30 users times 7 products) is too small for Mahout to work with? Look forward to your comments. Thanks.
