Re: Negative Preferences in a Recommender
Yes the model has no room for literally negative input. I think that conceptually people do want negative input, and in this model, negative numbers really are the natural thing to express that. You could give negative input a small positive weight. Or extend the definition of c so that it is merely small, not negative, when r is negative. But this was generally unsatisfactory. It has a logic, that even negative input is really a slightly positive association in the scheme of things, but the results were viewed as unintuitive. I ended up extending it to handle negative input more directly, such that negative input is read as evidence that p=0, instead of evidence that p=1. This works fine, and tidier than an ensemble (although that's a sound idea too). The change is quite small. Agree with the second point that learning weights is manual and difficult; that's unavoidable I think when you want to start adding different data types anyway. I also don't use M/R for searching parameter space since you may try a thousand combinations and each is a model build from scratch. I use a sample of data and run in-core. On Tue, Jun 18, 2013 at 2:30 AM, Dmitriy Lyubimov dlie...@gmail.com wrote: (Kinda doing something very close. ) Koren-Volynsky paper on implicit feedback can be generalized to decompose all input into preference (0 or 1) and confidence matrices (which is essentually an observation weight matrix). If you did not get any observations, you encode it as (p=0,c=1) but if you know that user did not like item, you can encode that observation with much more confidence weight, something like (p=0, c=30) -- actually as high confidence as a conversion in your case it seems. The problem with this is that you end up with quite a bunch of additional parameters in your model to figure, i.e. confidence weights for each type of action in the system. You can establish that thru extensive crossvalidation search, which is initially quite expensive (even for distributed machine cluster tech), but could be incrementally bail out much sooner after previous good guess is already known. MR doesn't work well for this though since it requires A LOT of iterations. On Mon, Jun 17, 2013 at 5:51 PM, Pat Ferrel pat.fer...@gmail.com wrote: In the case where you know a user did not like an item, how should the information be treated in a recommender? Normally for retail recommendations you have an implicit 1 for a purchase and no value otherwise. But what if you knew the user did not like an item? Maybe you have records of I want my money back for this junk reactions. You could make a scale, 0, 1 where 0 means a bad rating and 1 a good, no value as usual means no preference? Some of the math here won't work though since usually no value implicitly = 0 so maybe -1 = bad, 1 = good, no preference implicitly = 0? Would it be better to treat the bad rating as a 1 and good as 2? This would be more like the old star rating method only we would know where the cutoff should be between a good review and bad (1.5) I suppose this could also be treated as another recommender in an ensemble where r = r_p - r_h, where r_h = predictions from I hate this product preferences? Has anyone found a good method?
K Mean Clustering on Two columns`
Hi Team How to do the K Mean Clustering on 2 selected Columns Line No,age,income,sex,city 1,22,1500,1,xxx, 2,54,13450,2,yyy - - - - - Like this Input Goes . But i need to do Clustering on Columns 2 and 3 How to do that ? I had tried using synthatic kmean Means But i am not able to extract the cluster ID with corresponding to Line No. Please help me Thanks and regards Syed Abdul Kather Thanks and Regards, S SYED ABDUL KATHER
RE: K Mean Clustering on Two columns`
Hi, I implemented something similar in the following way. Created a class which implements org.apache.commons.math3.ml.clustering.Clusterable with just two member variables double[] point and long id and geter/setter function. Iterated through the data and created instances of this class. Added them to a list Then instantiated KMeansPlusPlusClusterer as below org.apache.commons.math3.ml.clustering.KMeansPlusPlusClustererCustomPoint clusterer = new KMeansPlusPlusClustererCustomPoint(4,100,new org.apache.commons.math3.ml.distance.CanberraDistance()); Then called KMeansPlusPlusClusterer.clusterer as follows ListCentroidClusterCustomPoint clusterList = clusterer.cluster(points); I was able to get the clusters in this way. Don't know whether this is the right approach. But it worked for me. Regards, Anand.C -Original Message- From: syed kather [mailto:in.ab...@gmail.com] Sent: Tuesday, June 18, 2013 3:23 PM To: user@mahout.apache.org Subject: K Mean Clustering on Two columns` Hi Team How to do the K Mean Clustering on 2 selected Columns Line No,age,income,sex,city 1,22,1500,1,xxx, 2,54,13450,2,yyy - - - - - Like this Input Goes . But i need to do Clustering on Columns 2 and 3 How to do that ? I had tried using synthatic kmean Means But i am not able to extract the cluster ID with corresponding to Line No. Please help me Thanks and regards Syed Abdul Kather Thanks and Regards, S SYED ABDUL KATHER
Re: Negative Preferences in a Recommender
I have found that in practice, don't-like is very close to like. That is, things that somebody doesn't like are very closely related to the things that they do like. Things that are quite distant wind up as don't-care, not don't-like. This makes most simple approaches to modeling polar preferences very dangerous. What I have usually done under the pressure of time is to consider like and don't-like to be equivalent synonyms and then maintain a kill list of items to not show. Works well pragmatically, but gives people hives when they hear of the details, especially if they actually believe humans act according to consistent philosophy. On Tue, Jun 18, 2013 at 9:13 AM, Sean Owen sro...@gmail.com wrote: Yes the model has no room for literally negative input. I think that conceptually people do want negative input, and in this model, negative numbers really are the natural thing to express that. You could give negative input a small positive weight. Or extend the definition of c so that it is merely small, not negative, when r is negative. But this was generally unsatisfactory. It has a logic, that even negative input is really a slightly positive association in the scheme of things, but the results were viewed as unintuitive. I ended up extending it to handle negative input more directly, such that negative input is read as evidence that p=0, instead of evidence that p=1. This works fine, and tidier than an ensemble (although that's a sound idea too). The change is quite small. Agree with the second point that learning weights is manual and difficult; that's unavoidable I think when you want to start adding different data types anyway. I also don't use M/R for searching parameter space since you may try a thousand combinations and each is a model build from scratch. I use a sample of data and run in-core. On Tue, Jun 18, 2013 at 2:30 AM, Dmitriy Lyubimov dlie...@gmail.com wrote: (Kinda doing something very close. ) Koren-Volynsky paper on implicit feedback can be generalized to decompose all input into preference (0 or 1) and confidence matrices (which is essentually an observation weight matrix). If you did not get any observations, you encode it as (p=0,c=1) but if you know that user did not like item, you can encode that observation with much more confidence weight, something like (p=0, c=30) -- actually as high confidence as a conversion in your case it seems. The problem with this is that you end up with quite a bunch of additional parameters in your model to figure, i.e. confidence weights for each type of action in the system. You can establish that thru extensive crossvalidation search, which is initially quite expensive (even for distributed machine cluster tech), but could be incrementally bail out much sooner after previous good guess is already known. MR doesn't work well for this though since it requires A LOT of iterations. On Mon, Jun 17, 2013 at 5:51 PM, Pat Ferrel pat.fer...@gmail.com wrote: In the case where you know a user did not like an item, how should the information be treated in a recommender? Normally for retail recommendations you have an implicit 1 for a purchase and no value otherwise. But what if you knew the user did not like an item? Maybe you have records of I want my money back for this junk reactions. You could make a scale, 0, 1 where 0 means a bad rating and 1 a good, no value as usual means no preference? Some of the math here won't work though since usually no value implicitly = 0 so maybe -1 = bad, 1 = good, no preference implicitly = 0? Would it be better to treat the bad rating as a 1 and good as 2? This would be more like the old star rating method only we would know where the cutoff should be between a good review and bad (1.5) I suppose this could also be treated as another recommender in an ensemble where r = r_p - r_h, where r_h = predictions from I hate this product preferences? Has anyone found a good method?
RE: Feature vector generation from Bag-of-Words
Hi,
Thanks. It did help.
Regards,
Anand.C
-Original Message-
From: Suneel Marthi [mailto:suneel_mar...@yahoo.com]
Sent: Tuesday, June 18, 2013 10:55 AM
To: Chandra Mohan, Ananda Vel Murugan; user@mahout.apache.org
Subject: Re: Feature vector generation from Bag-of-Words
Check this link -
http://stackoverflow.com/questions/13663567/mahout-csv-to-vector-and-running-the-program
From: Chandra Mohan, Ananda Vel Murugan ananda.muru...@honeywell.com
To: user@mahout.apache.org user@mahout.apache.org; Suneel Marthi
suneel_mar...@yahoo.com
Sent: Tuesday, June 18, 2013 12:59 AM
Subject: RE: Feature vector generation from Bag-of-Words
Hi,
I am implementing slightly different variation of this solution. I need some
guidance.
I have a CSV file with two columns, REMARKS and CATEGORY. Based on the remarks,
I train naïve bayes model which would automatically assign categories to
REMARKS. I followed this link
http://chimpler.wordpress.com/2013/03/13/using-the-mahout-naive-bayes-classifier-to-automatically-classify-twitter-messages/
It works fine.
Now I have a slightly different requirement. I want the text in REMARKS column
to be tokenized in a different fashion. I have some keywords. When those
keywords occur in REMARKS text, I want them to be intact and splitted further.
For example, if REMARKS text is Sump pressure is low, with default analyzer,
it would be split into four tokens as Sump, pressure, is, low. But I
want it to be tokenized as Sump pressure, is, low. I have implemented a
custom tokenizer which would do this.
Now I want to vectorize this. I tried the pseudo code suggested below. I don't
know how to serialize these vectors into sequence files. When I run seq2sparse,
apart from vectors, it creates some other labelindex and dictionary files. I
could not see the code to create those files here. Am I missing something? I
have started looking into org.apache.mahout.vectorizer package. Any pointers
would be of great help.
Regards,
Anand.C
-Original Message-
From: Suneel Marthi [mailto:suneel_mar...@yahoo.com]
Sent: Tuesday, May 21, 2013 10:21 PM
To: user@mahout.apache.org
Subject: Re: Feature vector generation from Bag-of-Words
It should be easy to convert the below pseudocode to MapReduce to scale for
large collection of documents.
From: Suneel Marthi suneel_mar...@yahoo.com
To: user@mahout.apache.org user@mahout.apache.org
Sent: Tuesday, May 21, 2013 12:20 PM
Subject: Re: Feature vector generation from Bag-of-Words
Stuti,
Here's how I would do it.
1. Create a collection of the 100 keywords that r of interest.
CollectionString keywords = new ArrayListString();
keywords.addAll(your 100 keywords);
2. For each word in each of the text documents create a Multiset (which is a
bag of words) ,
retain only those terms of interest from (1) that are of interest and use
Mahout's StaticWordValu
// Itertate through all the documents
for document in documents {
//create a bag of words for each document
MultisetString multiset = new HashMultisetString();
// create a RandomAccessSparseVector
Vector v = new RandomAccessSparseVector(100); // 100 features for the 100
keywords
for term in document.terms {
multiset.add(term);
}
// retain only those keywords that are of interest (from step 1)
multiset.retainAll(keywords);
// You now have a bag of words containing only the keywords with their
term frequencies
// Use one of the Feature Encoders, refer to Section 14.3 of Mahout in
Action for more detailed description of
// this process
FeatureVectorEncoder encoder = new StaticWordValueEncoder(body);
for (Multiset.EntryString entry : multiset.entrySet()) {
encoder.addToVector(entry.getElement(), entry.getCount(), v);
}
From: Stuti Awasthi stutiawas...@hcl.com
To: user@mahout.apache.org user@mahout.apache.org
Sent: Tuesday, May 21, 2013 7:17 AM
Subject: Feature vector generation from Bag-of-Words
Hi all,
I have a query regarding the Feature Vector generation for Text documents.
I have read Mahout in Action and understood how to create the text document in
feature vector weighed by Tf of Tfidf schemes. My usecase is a little tweaked
with that.
I have few keywords may be say 100 and I want to create the Feature Vector of
the text documents only with these 100 keywords. So I would like to calculate
the frequency of each keyword in each document and generate the feature vector
of the keyword with the frequency as weights.
Is there any already present way to do this or Il need to write the custom code?
Thanks
Stuti Awasthi
::DISCLAIMER::
Re: Negative Preferences in a Recommender
To your point Ted, I was surprised to find that remove-from-cart actions predicted sales almost as well as purchases did but it also meant filtering from recs. We got the best scores treating them as purchases and not recommending them again. No one pried enough to get get bothered. In this particular case I'm ingesting movie reviews, thumbs up or down. I'm trying to prime the pump for a cold start case of a media guide app with expert reviews but no users yet. Expert reviewers review everything so I don't think there will be much goodness in treating a thumbs down like a thumbs up in this particular case. Sean, are you suggesting that negative reviews might be modeled as a 0 rather than no value? Using the Mahout recommender this will only show up in filtering the negatives out of recs as Ted suggests, right? Since a 0 preference would mean, don't recommend, just as a preference of 1 would. This seems like a good approach but I may have missed something in your suggestion. In this case I'm not concerned with recommending to experts, I'm trying to make good recs to new users with few thumbs up or down by comparing them to experts with lots of thumbs up and down.The similarity metric will have new users with only a few preferences and will compare them to reviewers with many many more. I wonder if this implies a similarity metric that uses only common values (cooccurrence) rather than the usual log-likelihood? I guess it's easy to try both. Papers I've read on this subject. The first has an interesting discussion of using experts in CF. http://www.slideshare.net/xamat/the-science-and-the-magic-of-user-feedback-for-recommender-systems http://www.sis.pitt.edu/~hlee/paper/umap2009_LeeBrusilovsky.pdf On Jun 18, 2013, at 3:48 AM, Ted Dunning ted.dunn...@gmail.com wrote: I have found that in practice, don't-like is very close to like. That is, things that somebody doesn't like are very closely related to the things that they do like. Things that are quite distant wind up as don't-care, not don't-like. This makes most simple approaches to modeling polar preferences very dangerous. What I have usually done under the pressure of time is to consider like and don't-like to be equivalent synonyms and then maintain a kill list of items to not show. Works well pragmatically, but gives people hives when they hear of the details, especially if they actually believe humans act according to consistent philosophy. On Tue, Jun 18, 2013 at 9:13 AM, Sean Owen sro...@gmail.com wrote: Yes the model has no room for literally negative input. I think that conceptually people do want negative input, and in this model, negative numbers really are the natural thing to express that. You could give negative input a small positive weight. Or extend the definition of c so that it is merely small, not negative, when r is negative. But this was generally unsatisfactory. It has a logic, that even negative input is really a slightly positive association in the scheme of things, but the results were viewed as unintuitive. I ended up extending it to handle negative input more directly, such that negative input is read as evidence that p=0, instead of evidence that p=1. This works fine, and tidier than an ensemble (although that's a sound idea too). The change is quite small. Agree with the second point that learning weights is manual and difficult; that's unavoidable I think when you want to start adding different data types anyway. I also don't use M/R for searching parameter space since you may try a thousand combinations and each is a model build from scratch. I use a sample of data and run in-core. On Tue, Jun 18, 2013 at 2:30 AM, Dmitriy Lyubimov dlie...@gmail.com wrote: (Kinda doing something very close. ) Koren-Volynsky paper on implicit feedback can be generalized to decompose all input into preference (0 or 1) and confidence matrices (which is essentually an observation weight matrix). If you did not get any observations, you encode it as (p=0,c=1) but if you know that user did not like item, you can encode that observation with much more confidence weight, something like (p=0, c=30) -- actually as high confidence as a conversion in your case it seems. The problem with this is that you end up with quite a bunch of additional parameters in your model to figure, i.e. confidence weights for each type of action in the system. You can establish that thru extensive crossvalidation search, which is initially quite expensive (even for distributed machine cluster tech), but could be incrementally bail out much sooner after previous good guess is already known. MR doesn't work well for this though since it requires A LOT of iterations. On Mon, Jun 17, 2013 at 5:51 PM, Pat Ferrel pat.fer...@gmail.com wrote: In the case where you know a user did not like an item, how should the information be treated in a recommender? Normally for retail
Re: Negative Preferences in a Recommender
They are on a lot of papers, which are you looking at? On Jun 17, 2013, at 6:30 PM, Dmitriy Lyubimov dlie...@gmail.com wrote: (Kinda doing something very close. ) Koren-Volynsky paper on implicit feedback can be generalized to decompose all input into preference (0 or 1) and confidence matrices (which is essentually an observation weight matrix). If you did not get any observations, you encode it as (p=0,c=1) but if you know that user did not like item, you can encode that observation with much more confidence weight, something like (p=0, c=30) -- actually as high confidence as a conversion in your case it seems. The problem with this is that you end up with quite a bunch of additional parameters in your model to figure, i.e. confidence weights for each type of action in the system. You can establish that thru extensive crossvalidation search, which is initially quite expensive (even for distributed machine cluster tech), but could be incrementally bail out much sooner after previous good guess is already known. MR doesn't work well for this though since it requires A LOT of iterations. On Mon, Jun 17, 2013 at 5:51 PM, Pat Ferrel pat.fer...@gmail.com wrote: In the case where you know a user did not like an item, how should the information be treated in a recommender? Normally for retail recommendations you have an implicit 1 for a purchase and no value otherwise. But what if you knew the user did not like an item? Maybe you have records of I want my money back for this junk reactions. You could make a scale, 0, 1 where 0 means a bad rating and 1 a good, no value as usual means no preference? Some of the math here won't work though since usually no value implicitly = 0 so maybe -1 = bad, 1 = good, no preference implicitly = 0? Would it be better to treat the bad rating as a 1 and good as 2? This would be more like the old star rating method only we would know where the cutoff should be between a good review and bad (1.5) I suppose this could also be treated as another recommender in an ensemble where r = r_p - r_h, where r_h = predictions from I hate this product preferences? Has anyone found a good method?
Re: Negative Preferences in a Recommender
I'm suggesting using numbers like -1 for thumbs-down ratings, and then using these as a positive weight towards 0, just like positive values are used as positive weighting towards 1. Most people don't make many negative ratings. For them, what you do with these doesn't make a lot of difference. It might for the few expert users, and they might be the ones that care. For me it was exactly this... user acceptance testers were pointing out that thumbs-down ratings didn't seem to have the desired effect, because they saw the result straight away. Here's an alternative structure that doesn't involve thumbs-down: choose 4 items, and sample in a way to prefer items that are distant in feature space. Ask the user to pick 1 that is most interesting. Repeat a few times. On Tue, Jun 18, 2013 at 3:55 PM, Pat Ferrel p...@occamsmachete.com wrote: To your point Ted, I was surprised to find that remove-from-cart actions predicted sales almost as well as purchases did but it also meant filtering from recs. We got the best scores treating them as purchases and not recommending them again. No one pried enough to get get bothered. In this particular case I'm ingesting movie reviews, thumbs up or down. I'm trying to prime the pump for a cold start case of a media guide app with expert reviews but no users yet. Expert reviewers review everything so I don't think there will be much goodness in treating a thumbs down like a thumbs up in this particular case. Sean, are you suggesting that negative reviews might be modeled as a 0 rather than no value? Using the Mahout recommender this will only show up in filtering the negatives out of recs as Ted suggests, right? Since a 0 preference would mean, don't recommend, just as a preference of 1 would. This seems like a good approach but I may have missed something in your suggestion. In this case I'm not concerned with recommending to experts, I'm trying to make good recs to new users with few thumbs up or down by comparing them to experts with lots of thumbs up and down.The similarity metric will have new users with only a few preferences and will compare them to reviewers with many many more. I wonder if this implies a similarity metric that uses only common values (cooccurrence) rather than the usual log-likelihood? I guess it's easy to try both. Papers I've read on this subject. The first has an interesting discussion of using experts in CF. http://www.slideshare.net/xamat/the-science-and-the-magic-of-user-feedback-for-recommender-systems http://www.sis.pitt.edu/~hlee/paper/umap2009_LeeBrusilovsky.pdf
Re: Negative Preferences in a Recommender
Koren, Volinsky: CF for implicit feedback datasets On Tue, Jun 18, 2013 at 8:07 AM, Pat Ferrel p...@occamsmachete.com wrote: They are on a lot of papers, which are you looking at? On Jun 17, 2013, at 6:30 PM, Dmitriy Lyubimov dlie...@gmail.com wrote: (Kinda doing something very close. ) Koren-Volynsky paper on implicit feedback can be generalized to decompose all input into preference (0 or 1) and confidence matrices (which is essentually an observation weight matrix). If you did not get any observations, you encode it as (p=0,c=1) but if you know that user did not like item, you can encode that observation with much more confidence weight, something like (p=0, c=30) -- actually as high confidence as a conversion in your case it seems. The problem with this is that you end up with quite a bunch of additional parameters in your model to figure, i.e. confidence weights for each type of action in the system. You can establish that thru extensive crossvalidation search, which is initially quite expensive (even for distributed machine cluster tech), but could be incrementally bail out much sooner after previous good guess is already known. MR doesn't work well for this though since it requires A LOT of iterations. On Mon, Jun 17, 2013 at 5:51 PM, Pat Ferrel pat.fer...@gmail.com wrote: In the case where you know a user did not like an item, how should the information be treated in a recommender? Normally for retail recommendations you have an implicit 1 for a purchase and no value otherwise. But what if you knew the user did not like an item? Maybe you have records of I want my money back for this junk reactions. You could make a scale, 0, 1 where 0 means a bad rating and 1 a good, no value as usual means no preference? Some of the math here won't work though since usually no value implicitly = 0 so maybe -1 = bad, 1 = good, no preference implicitly = 0? Would it be better to treat the bad rating as a 1 and good as 2? This would be more like the old star rating method only we would know where the cutoff should be between a good review and bad (1.5) I suppose this could also be treated as another recommender in an ensemble where r = r_p - r_h, where r_h = predictions from I hate this product preferences? Has anyone found a good method?
CFP: ACM RecSys 2013 Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys 2013)
Dear colleagues, we are pleased to announce RepSys, a workshop on Reproducibility and Replication that will be held in ACM RecSys 2013. This workshop aims to provide an opportunity to discuss about the limitations and challenges of experimental reproducibility and replication. Hope you find it interesting. Regards, Alejandro [Apologies if you receive this more than once] === ACM RecSys Workshop on Reproducibility and Replication in Recommender Systems Evaluation - RepSys2013 7th ACM Recommender Systems Conference (RecSys 2013) Hong Kong, China, 12 or 16 October 2013 http://repsys.project.cwi.nl === * Submission deadline: 22 July 2013 * == Scope == Experiment replication and reproduction are key requirements for empirical research methodology, and an important open issue in the field of Recommender Systems. When an experiment is repeated by a different researcher and exactly the same result is obtained, we can say the experiment has been replicated. When the results are not exactly the same but the conclusions are compatible with the prior ones, we have a reproduction of the experiment. Reproducibility and replication involve recommendation algorithm implementations, experimental protocols, and evaluation metrics. While the problem of reproducibility and replication has been recognized in the Recommender Systems community, the need for a clear solution remains largely unmet, which motivates the present workshop. == Topics == We invite the submission of papers reporting original research, studies, advances, experiences, or work in progress in the scope of reproducibility and replication in Recommender Systems evaluation. Papers explicitly dealing with replication of previously published experimental conditions/algorithms/metrics and the resulting analysis are encouraged. In particular, we seek discussions on the difficulties the authors may find in this process, along with their limitations or successes on reproducing the original results. The topics the workshop seeks to address include –though need not be limited to– the following: * Limitations and challenges of experimental reproducibility and replication * Reproducible experimental design * Replicability of algorithms * Standardization of metrics: definition and computation protocols * Evaluation software: frameworks, utilities, services * Reproducibility in user-centric studies * Datasets and benchmarks * Recommender software reuse * Replication of already published work * Reproducibility within and across domains and organizations * Reproducibility and replication guidelines == Submission == Two submission types are accepted: long papers of up to 8 pages, and short papers up to 4 pages. The papers will be evaluated for their originality, contribution significance, soundness, clarity, and overall quality. The interest of contributions will be assessed in terms of technical and scientific findings, contribution to the knowledge and understanding of the problem, methodological advancements, or applicative value. Specific contributions focusing on repeatability and reproducibility in terms of algorithm implementations, evaluation frameworks and/or practice will also be welcome and valued. All submissions shall adhere to the standard ACM SIG proceedings format: http://www.acm.org/sigs/publications/proceedings-templates. Submissions shall be sent as a pdf file through the online submission system now open at: https://www.easychair.org/conferences/?conf=repsys2013. == Important dates == * Paper submission deadline: 22 July * Notification: 16 August * Camera-ready version due: 30 August == Organizers == * Alejandro Bellogín, Centrum Wiskunde Informatica, The Netherlands * Pablo Castells, Universidad Autónoma de Madrid, Spain * Alan Said, Centrum Wiskunde Informatica, The Netherlands * Domonkos Tikk, Gravity RD, Hungary == Programme Committee == * Xavier Amatriain, Netflix, USA * Linas Baltrunas, Telefonica Research, Spain * Marcel Blattner, University of Applied Sciences, Switzerland * Iván Cantador, Universidad Autónoma de Madrid, Spain * Ed Chi, Google, USA * Arjen de Vries, Centrum Wiskunde Informatica, Netherlands * Juan Manuel Fernández, Universidad de Granada, Spain * Zeno Gantner, Nokia, Germany * Pankaj Gupta, Twitter, USA * Andreas Hotho, University of Würburg, Germany * Juan Huete, Universidad de Granada, Spain * Kris Jack, Mendeley, England * Dietmar Jannach, University of Dortmund, Germany * Jaap Kamps, University of Amsterdam, Netherlands * Alexandros Karatzoglou, TID, Spain * Bart Knijnenburg, University of California, Irvine, USA * Ido Guy, Google, Israel * Jérôme
Re: Negative Preferences in a Recommender
On Tue, Jun 18, 2013 at 3:48 AM, Ted Dunning ted.dunn...@gmail.com wrote: I have found that in practice, don't-like is very close to like. That is, things that somebody doesn't like are very closely related to the things that they do like. I guess it makes sense for cancellations. i guess it should become pretty obvious from extensive crossvalidation search. Things that are quite distant wind up as don't-care, not don't-like. This makes most simple approaches to modeling polar preferences very dangerous. What I have usually done under the pressure of time is to consider like and don't-like to be equivalent synonyms and then maintain a kill list of items to not show. Works well pragmatically, but gives people hives when they hear of the details, especially if they actually believe humans act according to consistent philosophy. Or we just don't know exact prevailing reason for the returns. :) did not fit is almost fit, give me similar, and found on another sale event means I still like it, just not your price. However, if there's a consistent quality issue, it may turn bad enough to consider p=0. bottom line, it should become fairly obvious which reasoning prevails, thru validation. Kill list should probably be maintained for a whole lot of reasons, not just returns. E.g. something that was recently bought, may be one-a-lifetime purchase, or it may be replenishable with a certain period of repeatability (which could also be modelled). Does it makes sense? On Tue, Jun 18, 2013 at 9:13 AM, Sean Owen sro...@gmail.com wrote: Yes the model has no room for literally negative input. I think that conceptually people do want negative input, and in this model, negative numbers really are the natural thing to express that. You could give negative input a small positive weight. Or extend the definition of c so that it is merely small, not negative, when r is negative. But this was generally unsatisfactory. It has a logic, that even negative input is really a slightly positive association in the scheme of things, but the results were viewed as unintuitive. I ended up extending it to handle negative input more directly, such that negative input is read as evidence that p=0, instead of evidence that p=1. This works fine, and tidier than an ensemble (although that's a sound idea too). The change is quite small. Agree with the second point that learning weights is manual and difficult; that's unavoidable I think when you want to start adding different data types anyway. I also don't use M/R for searching parameter space since you may try a thousand combinations and each is a model build from scratch. I use a sample of data and run in-core. On Tue, Jun 18, 2013 at 2:30 AM, Dmitriy Lyubimov dlie...@gmail.com wrote: (Kinda doing something very close. ) Koren-Volynsky paper on implicit feedback can be generalized to decompose all input into preference (0 or 1) and confidence matrices (which is essentually an observation weight matrix). If you did not get any observations, you encode it as (p=0,c=1) but if you know that user did not like item, you can encode that observation with much more confidence weight, something like (p=0, c=30) -- actually as high confidence as a conversion in your case it seems. The problem with this is that you end up with quite a bunch of additional parameters in your model to figure, i.e. confidence weights for each type of action in the system. You can establish that thru extensive crossvalidation search, which is initially quite expensive (even for distributed machine cluster tech), but could be incrementally bail out much sooner after previous good guess is already known. MR doesn't work well for this though since it requires A LOT of iterations. On Mon, Jun 17, 2013 at 5:51 PM, Pat Ferrel pat.fer...@gmail.com wrote: In the case where you know a user did not like an item, how should the information be treated in a recommender? Normally for retail recommendations you have an implicit 1 for a purchase and no value otherwise. But what if you knew the user did not like an item? Maybe you have records of I want my money back for this junk reactions. You could make a scale, 0, 1 where 0 means a bad rating and 1 a good, no value as usual means no preference? Some of the math here won't work though since usually no value implicitly = 0 so maybe -1 = bad, 1 = good, no preference implicitly = 0? Would it be better to treat the bad rating as a 1 and good as 2? This would be more like the old star rating method only we would know where the cutoff should be between a good review and bad (1.5) I suppose this could also be treated as another recommender in an ensemble where r = r_p - r_h, where r_h = predictions from I hate this product preferences? Has anyone found a good method?
