Your product is subject to seasonality constraints (which teas are likely right now) and repeat buying. I would separate out the recommendation of repeat buys from the separation of new items.
You may also find that item-item links on your web site are helpful. These are easy to get using this system. On Mon, Nov 21, 2011 at 11:46 AM, Manuel Blechschmidt < [email protected]> wrote: > 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 > >
