Hello Ted, thanks for these advices. I hope that the open source and research community will conduct more user studies and provide the results. There is still a lack for this.
There are a lot of problems which can only be solved by learning from the user interaction not only from RMSE. Great stuff as soon as I have more I will try to post the results on this list. /Manuel Ted Dunning <[email protected]> schrieb: >Filtering recommendations lists is incredibly important. What you are >doing is pretty straightforward with post-processing of the recommended >list. > >Other things that I often recommend include: > >- dithering. This is partial randomization of your results list that moves >items deep in the list higher, but mostly leaves the top items in place. > This helps your algorithm explore more and helps avoid the problem of >people never clicking to the second page. Dithering can make more >difference than all but the largest algorithm differences. > >- anti-flood. It is important to not have a results list be dominated by a >single kind of thing. The segregation of your email is a form of this. I >often implement this by downgrading the scores of items very similar to >higher scoring items. In some domains this makes a night and day >difference. > >On Mon, Nov 21, 2011 at 3:28 PM, Manuel Blechschmidt < >[email protected]> wrote: > >> Thanks for the answer Ted. >> >> On 21.11.2011, at 16:20, Ted Dunning wrote: >> >> > 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. >> >> Actually I want to generate an email with diverse recommendations. >> >> Something like: >> >> Your personal top sellers: >> .. 3 items ... >> >> Special Winter Sales: >> ... 3 items ... >> >> This might be interesting for you: >> ... 6 items ... >> >> This is new in our store: >> ... 3 items ... >> >> > >> > You may also find that item-item links on your web site are helpful. >> These >> > are easy to get using this system. >> >> Yes, actually the website is already using some very basic item-to-item >> recommendations. So I am more interested in the newsletter part especially >> because I can track which items are really attractive and which aren't. >> >> /Manuel >> >> > >> > 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 >> >> >> >> >> >> -- >> Manuel Blechschmidt >> Dortustr. 57 >> 14467 Potsdam >> Mobil: 0173/6322621 >> Twitter: http://twitter.com/Manuel_B >> >>
