Hello Sean, On 21.11.2011, at 12:16, Sean Owen wrote:
> Yes, because you have fewer items, an item-item-similarity-based algorithm > probably runs much faster. Thanks for your blazing fast feedback. > > I would not necessarily use the raw number of kg as a preference. It's not > really true that someone who buys 10kg of an item likes it 10x more than > one he buys 1kg of. Maybe the second spice is much more valuable? I would > at least try taking the logarithm of the weight, but, I think this is very > noisy as a proxy for "preference". It creates illogical leaps -- because > one user bought 85kg of X, and Y is "similar" to X, this would conclude > that you're somewhat likely to buy 85kg of Y too. I would probably not use > weight at all this way. Thanks for this suggestions. I will consider to integrate a logarithmic weight into the recommender. At the moment I am more concerned to get the user feedback component working. From some manual tests I can already tell that the recommendation for some users make sense. Based on my own profile I can tell that when I buy more of a certain product then I also like the product more. I am also thinking about some seasonal tweaking. Tea is a very seasonal product during winter and christmas other flavors are sold then in summer. http://diuf.unifr.ch/main/is/sites/diuf.unifr.ch.main.is/files/documents/publications/WS07-08-011.pdf > > It is not therefore surprising that log-likelihood works well, since it > ignores this value actually. > > (You mentioned RMSE but your evaluation metric is > average-absolute-difference -- L1, not L2). You are right RMSE (root-mean-squared-error) is wrong. I think it is MEA (mean-avagerage-error). > > This is quite a small data set so you should have no performance issues. > Your evaluations, which run over all users in the data set, are taking mere > seconds. I am sure you could get away with much less memory/processing if > you like. This is by far good enough. The more important part is the newsletter sending. I have to generate about 10.000 emails that makes more headache then the recommender. /Manuel > > > On Mon, Nov 21, 2011 at 11:06 AM, Manuel Blechschmidt < > [email protected]> wrote: > >> Hello Mahout Team, hello users, >> me and a friend are currently evaluating recommendation techniques for >> personalizing a newsletter for a company selling tea, spices and some other >> products. Mahout is such a great product which saves me hours of time and >> millions of money because I want to give something back I write this small >> case study to the mailing list. >> >> I am conducting an offline testing of which recommender is the most >> accurate one. Further I am interested in run time behavior like memory >> consumption and runtime. >> >> The data contains implicit feedback. The preferences of the user is the >> amount in gramm that he bought from a certain product (453 g ~ 1 pound). If >> a certain product does not have this data it is replaced with 50. So >> basically I want mahout to predict how much of a certain product is a user >> buying next. This is also helpful for demand planing. I am currently not >> using any time data because I did not find a recommender which is using >> this data. >> >> Users: 12858 >> Items: 5467 >> 121304 preferences >> MaxPreference: 85850.0 (Meaning that there is someone who ordered 85 kg of >> a certain tea or spice) >> MinPreference: 50.0 >> >> Here are the pure benchmarks for accuracy in RMSE. They change during >> every run of the evaluation (~15%): >> >> Evaluation of randomBased (baseline): 43045.380570443434 >> (RandomRecommender(model)) (Time: ~0.3 s) (Memory: 16MB) >> Evaluation of ItemBased with Pearson Correlation: 315.5804958647985 >> (GenericItemBasedRecommender(model, PearsonCorrelationSimilarity(model)) >> (Time: ~1s) (Memory: 35MB) >> Evaluation of ItemBase with uncentered Cosine: 198.25393235323375 >> (GenericItemBasedRecommender(model, UncenteredCosineSimilarity(model))) >> (Time: ~1s) (Memory: 32MB) >> Evaluation of ItemBase with log likelihood: 176.45243607278724 >> (GenericItemBasedRecommender(model, LogLikelihoodSimilarity(model))) >> (Time: ~5s) (Memory: 42MB) >> Evaluation of UserBased 3 with Pearson Correlation: 1378.1188069379868 >> (GenericUserBasedRecommender(model, NearestNUserNeighborhood(3, >> PearsonCorrelationSimilarity(model), model), >> PearsonCorrelationSimilarity(model))) (Time: ~52s) (Memory: 57MB) >> Evaluation of UserBased 20 with Pearson Correlation: 1144.1905989614288 >> (GenericUserBasedRecommender(model, NearestNUserNeighborhood(20, >> PearsonCorrelationSimilarity(model), model), >> PearsonCorrelationSimilarity(model))) (Time: ~51s) (Memory: 57MB) >> Evaluation of SlopeOne: 464.8989330869532 (SlopeOneRecommender(model)) >> (Time: ~4s) (Memory: 604MB) >> Evaluation of SVD based: 326.1050823499026 (ALSWRFactorizer(model, 100, >> 0.3, 5)) (Time: ) (Memory: 691MB) >> >> These were measured with the following method: >> >> RecommenderEvaluator evaluator = new >> AverageAbsoluteDifferenceRecommenderEvaluator(); >> double evaluation = evaluator.evaluate(randomBased, null, myModel, >> 0.9, 1.0); >> >> Memory usage was about 50m with the item based case. Slope One and SVD >> base seams to use the most memory (615MB & 691MB). >> >> The performance differs a lot. The fastest ones where the item based. They >> took about 1 to 5 seconds (PearsonCorrelationSimilarity and >> UncenteredCosineSimilarity 1 s, LogLikelihoodSimilarity 5s) >> The user based where a lot slower. >> >> Conclusion is that in my case the item based approach is the fastest, >> lowest memory consumption and most accurate one. Further I can use the >> recommendedBecause function. >> >> Here is the spec of the computer: >> 2.3GHz Intel Core i5 (4 Cores). 1024 MB for java virtual machine. >> >> In the next step, probably in the next 2 month. I have to design a >> newsletter and send it to the customers. Then I can benchmark the user >> acceptance rate of the recommendations. >> >> Any suggestions for enhancements are appreciated. If anybody is interested >> in the dataset or the evaluation code send me a private email. I might be >> able to convince the company to give out the dataset if the person is doing >> some interesting research. >> >> /Manuel >> -- >> Manuel Blechschmidt >> Dortustr. 57 >> 14467 Potsdam >> Mobil: 0173/6322621 >> Twitter: http://twitter.com/Manuel_B >> >> -- Manuel Blechschmidt Dortustr. 57 14467 Potsdam Mobil: 0173/6322621 Twitter: http://twitter.com/Manuel_B
