Re: Clustering product views and sales
You always will have a cold start problem for a subset of users--the new ones to a site. Popularity doesn't always work either. Sometimes you have a flat purchase frequency distribution, as I've seen. In these cases a metadata or content based recommender is nice to fill in. If you have no metadata you still have items similarities (based on older users purchases and views). I think one important thing to think about is that you don't always need to have recommendations based on the user's history. You may find that you get better results by using item similarity based recommendations. So on an item page you can show recommendations with the above techniques in a wide variety of situations. On another subject looking at the predictive power of views (for purchases) and purchases (for purchases) you will likely find views a weak predictor. I think what Ted is talking about below is a technique for using a co-occurrence matrix to find views that lead to purchases. To use this you would build two models, one from purchases and one from the co-ocurrence of views with purchases. Then you will need to combine the weights of recommendations from both models for a given user history OR similarities for a given item. The conversation Johannes sites below has some details http://markmail.org/message/5cfewal3oyt6vw2k I have a working cross-recommender made for using views and purchases. The next question is how how to measure its performance. There are ways to simulate the view-purchase data and other uses for the cross-recommender technique. But having a real view and purchase dataset would be incredibly useful! I keep begging people on this list... Can you share your data? If so I'd be happy to share the code (actually I'll put it on github eventually). On May 6, 2013, at 9:40 PM, Johannes Schulte johannes.schu...@gmail.com wrote: Hi! As a starting point I remember this conversation containing both elements (although the reconstruction part is rather small, hint!) http://markmail.org/message/5cfewal3oyt6vw2k On Tue, May 7, 2013 at 1:00 AM, Dominik Hübner cont...@dhuebner.com wrote: One more thing for now @Ted: What do you refer to with sparsification and reconstruction? On May 7, 2013, at 12:19 AM, Ted Dunning ted.dunn...@gmail.com wrote: Truly cold start is best handled by recommending the most popular items. If you know *anything* at all such as geo or browser or OS, then you can use that to recommend using conventional techniques (that is, you can recommend for the characteristics rather than for the person). Within a very few interactions, however, real recommendations will kick in. My lately preferred approach is to derive indicators using sparsification or ALS+reconstruction. These indicators can be historical items or static items such as geo information. These indicators can be combined in a single step using a search engine. On Mon, May 6, 2013 at 2:58 PM, Dominik Hübner cont...@dhuebner.com wrote: The cluster was mostly intended for tackling the cold start problem for new users. I want to build a recommender based on existing components or to be precise a combination of them. Unfortunately, the only product meta-data I currently have is the product price. Furthermore, this is a project I am working on alone. As a consequence, the approaches I can examine in the given time are limited. Would using ALS and ranking its outcome by e.g. frequent item set algorithms be something worth looking into? Or did you mean something different? My personal goal is to build a recommender providing acceptable results using the data I currently have available. Of course, this will only serve as a basis for further improvements where necessary or if further information can be obtained. On May 6, 2013, at 11:21 PM, Ted Dunning ted.dunn...@gmail.com wrote: Are you looking to build a product recommender based on your own design? Or do you want to build one based on existing methods? If you want to use existing methods, clustering has essentially no role. I think that composite approaches that use item meta-data and different kinds of behavioral cues are important to best performance. On Mon, May 6, 2013 at 12:35 PM, Dominik Hübner cont...@dhuebner.com wrote: Well, as you already might have guessed, I am building a product recommender system for my thesis. I am planning to evaluate ALS (both, implicit and explicit) as well as item -similarity recommendation for users with at least a few known products. Nevertheless, the majority of users only has seen a single (or 2-3) product(s). I want to recommend them the most popular items from clusters, their only product comes from (as a workaround for the cold-start problem). Furthermore, I expect to be able to see which kind of products users like. This might provide me some information about how well ALS and similarity recommenders fit the user's
Re: Clustering product views and sales
And running the clustering on the cooccurrence matrix or doing PCA by removing eigenvalues/vectors? On May 6, 2013, at 8:52 PM, Ted Dunning ted.dunn...@gmail.com wrote: On Mon, May 6, 2013 at 11:29 AM, Dominik Hübner cont...@dhuebner.comwrote: Oh, and I forgot how the views and sales are used to build product vectors. As of now, I implemented binary vectors, vectors counting the number of views and sales (e.g 1view=1count, 1sale=10counts) and ordinary vectors ( view = 1, sale=5). I would recommend just putting the view and sale in different columns and doing cooccurrence analysis on this.
Re: Clustering product views and sales
I don't even think that clustering is all that necessary. The reduced cooccurrence matrix will give you items related to each item. You can use something like PCA, but SVD is just as good here due to near zero mean. You could SSVD or ALS from Mahout to do this analysis and then use k-means on the right singular vectors (aka item representation). What is the high level goal that you are trying to solve with this clustering? On Mon, May 6, 2013 at 12:01 PM, Dominik Hübner cont...@dhuebner.comwrote: And running the clustering on the cooccurrence matrix or doing PCA by removing eigenvalues/vectors? On May 6, 2013, at 8:52 PM, Ted Dunning ted.dunn...@gmail.com wrote: On Mon, May 6, 2013 at 11:29 AM, Dominik Hübner cont...@dhuebner.com wrote: Oh, and I forgot how the views and sales are used to build product vectors. As of now, I implemented binary vectors, vectors counting the number of views and sales (e.g 1view=1count, 1sale=10counts) and ordinary vectors ( view = 1, sale=5). I would recommend just putting the view and sale in different columns and doing cooccurrence analysis on this.
Re: Clustering product views and sales
Well, as you already might have guessed, I am building a product recommender system for my thesis. I am planning to evaluate ALS (both, implicit and explicit) as well as item -similarity recommendation for users with at least a few known products. Nevertheless, the majority of users only has seen a single (or 2-3) product(s). I want to recommend them the most popular items from clusters, their only product comes from (as a workaround for the cold-start problem). Furthermore, I expect to be able to see which kind of products users like. This might provide me some information about how well ALS and similarity recommenders fit the user's area of interest (an early evaluation) or at least to estimate if the chosen approach will work in some way. On May 6, 2013, at 9:09 PM, Ted Dunning ted.dunn...@gmail.com wrote: I don't even think that clustering is all that necessary. The reduced cooccurrence matrix will give you items related to each item. You can use something like PCA, but SVD is just as good here due to near zero mean. You could SSVD or ALS from Mahout to do this analysis and then use k-means on the right singular vectors (aka item representation). What is the high level goal that you are trying to solve with this clustering? On Mon, May 6, 2013 at 12:01 PM, Dominik Hübner cont...@dhuebner.comwrote: And running the clustering on the cooccurrence matrix or doing PCA by removing eigenvalues/vectors? On May 6, 2013, at 8:52 PM, Ted Dunning ted.dunn...@gmail.com wrote: On Mon, May 6, 2013 at 11:29 AM, Dominik Hübner cont...@dhuebner.com wrote: Oh, and I forgot how the views and sales are used to build product vectors. As of now, I implemented binary vectors, vectors counting the number of views and sales (e.g 1view=1count, 1sale=10counts) and ordinary vectors ( view = 1, sale=5). I would recommend just putting the view and sale in different columns and doing cooccurrence analysis on this.
Re: Clustering product views and sales
Since Dominik mentioned item-based and ALS, let me throw in a question here. I believe that one of the Netflix price solutions combined KNN and ALS. 1) What is the best way to combine the results of both? 2) Is there really merit to this approach? 3) Are there other combinations that make sense? (user-based + item-based)? On Mon, May 6, 2013 at 3:35 PM, Dominik Hübner cont...@dhuebner.com wrote: Well, as you already might have guessed, I am building a product recommender system for my thesis. I am planning to evaluate ALS (both, implicit and explicit) as well as item -similarity recommendation for users with at least a few known products. Nevertheless, the majority of users only has seen a single (or 2-3) product(s). I want to recommend them the most popular items from clusters, their only product comes from (as a workaround for the cold-start problem). Furthermore, I expect to be able to see which kind of products users like. This might provide me some information about how well ALS and similarity recommenders fit the user's area of interest (an early evaluation) or at least to estimate if the chosen approach will work in some way. On May 6, 2013, at 9:09 PM, Ted Dunning ted.dunn...@gmail.com wrote: I don't even think that clustering is all that necessary. The reduced cooccurrence matrix will give you items related to each item. You can use something like PCA, but SVD is just as good here due to near zero mean. You could SSVD or ALS from Mahout to do this analysis and then use k-means on the right singular vectors (aka item representation). What is the high level goal that you are trying to solve with this clustering? On Mon, May 6, 2013 at 12:01 PM, Dominik Hübner cont...@dhuebner.com wrote: And running the clustering on the cooccurrence matrix or doing PCA by removing eigenvalues/vectors? On May 6, 2013, at 8:52 PM, Ted Dunning ted.dunn...@gmail.com wrote: On Mon, May 6, 2013 at 11:29 AM, Dominik Hübner cont...@dhuebner.com wrote: Oh, and I forgot how the views and sales are used to build product vectors. As of now, I implemented binary vectors, vectors counting the number of views and sales (e.g 1view=1count, 1sale=10counts) and ordinary vectors ( view = 1, sale=5). I would recommend just putting the view and sale in different columns and doing cooccurrence analysis on this.
Re: Clustering product views and sales
On Mon, May 6, 2013 at 12:50 PM, Koobas koo...@gmail.com wrote: Since Dominik mentioned item-based and ALS, let me throw in a question here. I believe that one of the Netflix price solutions combined KNN and ALS. 1) What is the best way to combine the results of both? I think that combinations are important, but I think that the combination of very similar kinds of algorithms working on essentially the same data has almost no practical impact. 2) Is there really merit to this approach? Yes, but. 3) Are there other combinations that make sense? (user-based + item-based)? Absolutely. But I really think that the real mileage for improvement comes from the following: a) combining different kinds of behavior into a single recommendation framework b) judicious use of dithering to improve exploration c) substantial UI improvements to gather additional exploratory data for the recommendation engine d) principled testing framework for design and algorithmic alternatives Minor algorithmic changes are almost not visible on the priority list. Once you hit pretty-good there are far more important things to take on in a real rec engine project.
Re: Clustering product views and sales
I think I see the picture now. Thanks! On Mon, May 6, 2013 at 5:25 PM, Ted Dunning ted.dunn...@gmail.com wrote: On Mon, May 6, 2013 at 12:50 PM, Koobas koo...@gmail.com wrote: Since Dominik mentioned item-based and ALS, let me throw in a question here. I believe that one of the Netflix price solutions combined KNN and ALS. 1) What is the best way to combine the results of both? I think that combinations are important, but I think that the combination of very similar kinds of algorithms working on essentially the same data has almost no practical impact. 2) Is there really merit to this approach? Yes, but. 3) Are there other combinations that make sense? (user-based + item-based)? Absolutely. But I really think that the real mileage for improvement comes from the following: a) combining different kinds of behavior into a single recommendation framework b) judicious use of dithering to improve exploration c) substantial UI improvements to gather additional exploratory data for the recommendation engine d) principled testing framework for design and algorithmic alternatives Minor algorithmic changes are almost not visible on the priority list. Once you hit pretty-good there are far more important things to take on in a real rec engine project.
Re: Clustering product views and sales
The cluster was mostly intended for tackling the cold start problem for new users. I want to build a recommender based on existing components or to be precise a combination of them. Unfortunately, the only product meta-data I currently have is the product price. Furthermore, this is a project I am working on alone. As a consequence, the approaches I can examine in the given time are limited. Would using ALS and ranking its outcome by e.g. frequent item set algorithms be something worth looking into? Or did you mean something different? My personal goal is to build a recommender providing acceptable results using the data I currently have available. Of course, this will only serve as a basis for further improvements where necessary or if further information can be obtained. On May 6, 2013, at 11:21 PM, Ted Dunning ted.dunn...@gmail.com wrote: Are you looking to build a product recommender based on your own design? Or do you want to build one based on existing methods? If you want to use existing methods, clustering has essentially no role. I think that composite approaches that use item meta-data and different kinds of behavioral cues are important to best performance. On Mon, May 6, 2013 at 12:35 PM, Dominik Hübner cont...@dhuebner.comwrote: Well, as you already might have guessed, I am building a product recommender system for my thesis. I am planning to evaluate ALS (both, implicit and explicit) as well as item -similarity recommendation for users with at least a few known products. Nevertheless, the majority of users only has seen a single (or 2-3) product(s). I want to recommend them the most popular items from clusters, their only product comes from (as a workaround for the cold-start problem). Furthermore, I expect to be able to see which kind of products users like. This might provide me some information about how well ALS and similarity recommenders fit the user's area of interest (an early evaluation) or at least to estimate if the chosen approach will work in some way. On May 6, 2013, at 9:09 PM, Ted Dunning ted.dunn...@gmail.com wrote: I don't even think that clustering is all that necessary. The reduced cooccurrence matrix will give you items related to each item. You can use something like PCA, but SVD is just as good here due to near zero mean. You could SSVD or ALS from Mahout to do this analysis and then use k-means on the right singular vectors (aka item representation). What is the high level goal that you are trying to solve with this clustering? On Mon, May 6, 2013 at 12:01 PM, Dominik Hübner cont...@dhuebner.com wrote: And running the clustering on the cooccurrence matrix or doing PCA by removing eigenvalues/vectors? On May 6, 2013, at 8:52 PM, Ted Dunning ted.dunn...@gmail.com wrote: On Mon, May 6, 2013 at 11:29 AM, Dominik Hübner cont...@dhuebner.com wrote: Oh, and I forgot how the views and sales are used to build product vectors. As of now, I implemented binary vectors, vectors counting the number of views and sales (e.g 1view=1count, 1sale=10counts) and ordinary vectors ( view = 1, sale=5). I would recommend just putting the view and sale in different columns and doing cooccurrence analysis on this.
Re: Clustering product views and sales
It sounds like you don't quite have a cold start problem. You have a few behaviors, a few views or clicks, not zero. So you really just need to find an approach that's quite comfortable with sparse input. A low-rank factorization model like ALS works fine in this case, for example. There's a circularity problem in thinking about solving this with clustering: if you have not enough data to recommend to users at the start, on what data are you clustering them before that? I don't think you need clustering either. (Of course, you can cluster easily from the representation you get out of something like a low-rank factorization. It can easily be an output rather than an 'input'.) As to evaluation, it a depends a little on what you mean by frequent item sets and evaluation. You say a result is good if it occurs frequently overall with other items the user viewed? It makes some sense, although it sounds like you're just testing if the recommender does exactly what a item-similarity-based recommender would do when based on co-occurrence between items. That is, if that's defined as the right answer, then save yourself the trouble and build the recommender to give exactly that answer? Usually you see if the model recommends back things the user actually viewed, that were held out of the training data. This has its own problems but presupposing a correct algorithm isn't one of them.
Re: Clustering product views and sales
Truly cold start is best handled by recommending the most popular items. If you know *anything* at all such as geo or browser or OS, then you can use that to recommend using conventional techniques (that is, you can recommend for the characteristics rather than for the person). Within a very few interactions, however, real recommendations will kick in. My lately preferred approach is to derive indicators using sparsification or ALS+reconstruction. These indicators can be historical items or static items such as geo information. These indicators can be combined in a single step using a search engine. On Mon, May 6, 2013 at 2:58 PM, Dominik Hübner cont...@dhuebner.com wrote: The cluster was mostly intended for tackling the cold start problem for new users. I want to build a recommender based on existing components or to be precise a combination of them. Unfortunately, the only product meta-data I currently have is the product price. Furthermore, this is a project I am working on alone. As a consequence, the approaches I can examine in the given time are limited. Would using ALS and ranking its outcome by e.g. frequent item set algorithms be something worth looking into? Or did you mean something different? My personal goal is to build a recommender providing acceptable results using the data I currently have available. Of course, this will only serve as a basis for further improvements where necessary or if further information can be obtained. On May 6, 2013, at 11:21 PM, Ted Dunning ted.dunn...@gmail.com wrote: Are you looking to build a product recommender based on your own design? Or do you want to build one based on existing methods? If you want to use existing methods, clustering has essentially no role. I think that composite approaches that use item meta-data and different kinds of behavioral cues are important to best performance. On Mon, May 6, 2013 at 12:35 PM, Dominik Hübner cont...@dhuebner.com wrote: Well, as you already might have guessed, I am building a product recommender system for my thesis. I am planning to evaluate ALS (both, implicit and explicit) as well as item -similarity recommendation for users with at least a few known products. Nevertheless, the majority of users only has seen a single (or 2-3) product(s). I want to recommend them the most popular items from clusters, their only product comes from (as a workaround for the cold-start problem). Furthermore, I expect to be able to see which kind of products users like. This might provide me some information about how well ALS and similarity recommenders fit the user's area of interest (an early evaluation) or at least to estimate if the chosen approach will work in some way. On May 6, 2013, at 9:09 PM, Ted Dunning ted.dunn...@gmail.com wrote: I don't even think that clustering is all that necessary. The reduced cooccurrence matrix will give you items related to each item. You can use something like PCA, but SVD is just as good here due to near zero mean. You could SSVD or ALS from Mahout to do this analysis and then use k-means on the right singular vectors (aka item representation). What is the high level goal that you are trying to solve with this clustering? On Mon, May 6, 2013 at 12:01 PM, Dominik Hübner cont...@dhuebner.com wrote: And running the clustering on the cooccurrence matrix or doing PCA by removing eigenvalues/vectors? On May 6, 2013, at 8:52 PM, Ted Dunning ted.dunn...@gmail.com wrote: On Mon, May 6, 2013 at 11:29 AM, Dominik Hübner cont...@dhuebner.com wrote: Oh, and I forgot how the views and sales are used to build product vectors. As of now, I implemented binary vectors, vectors counting the number of views and sales (e.g 1view=1count, 1sale=10counts) and ordinary vectors ( view = 1, sale=5). I would recommend just putting the view and sale in different columns and doing cooccurrence analysis on this.
Re: Clustering product views and sales
One more thing for now @Ted: What do you refer to with sparsification and reconstruction? On May 7, 2013, at 12:19 AM, Ted Dunning ted.dunn...@gmail.com wrote: Truly cold start is best handled by recommending the most popular items. If you know *anything* at all such as geo or browser or OS, then you can use that to recommend using conventional techniques (that is, you can recommend for the characteristics rather than for the person). Within a very few interactions, however, real recommendations will kick in. My lately preferred approach is to derive indicators using sparsification or ALS+reconstruction. These indicators can be historical items or static items such as geo information. These indicators can be combined in a single step using a search engine. On Mon, May 6, 2013 at 2:58 PM, Dominik Hübner cont...@dhuebner.com wrote: The cluster was mostly intended for tackling the cold start problem for new users. I want to build a recommender based on existing components or to be precise a combination of them. Unfortunately, the only product meta-data I currently have is the product price. Furthermore, this is a project I am working on alone. As a consequence, the approaches I can examine in the given time are limited. Would using ALS and ranking its outcome by e.g. frequent item set algorithms be something worth looking into? Or did you mean something different? My personal goal is to build a recommender providing acceptable results using the data I currently have available. Of course, this will only serve as a basis for further improvements where necessary or if further information can be obtained. On May 6, 2013, at 11:21 PM, Ted Dunning ted.dunn...@gmail.com wrote: Are you looking to build a product recommender based on your own design? Or do you want to build one based on existing methods? If you want to use existing methods, clustering has essentially no role. I think that composite approaches that use item meta-data and different kinds of behavioral cues are important to best performance. On Mon, May 6, 2013 at 12:35 PM, Dominik Hübner cont...@dhuebner.com wrote: Well, as you already might have guessed, I am building a product recommender system for my thesis. I am planning to evaluate ALS (both, implicit and explicit) as well as item -similarity recommendation for users with at least a few known products. Nevertheless, the majority of users only has seen a single (or 2-3) product(s). I want to recommend them the most popular items from clusters, their only product comes from (as a workaround for the cold-start problem). Furthermore, I expect to be able to see which kind of products users like. This might provide me some information about how well ALS and similarity recommenders fit the user's area of interest (an early evaluation) or at least to estimate if the chosen approach will work in some way. On May 6, 2013, at 9:09 PM, Ted Dunning ted.dunn...@gmail.com wrote: I don't even think that clustering is all that necessary. The reduced cooccurrence matrix will give you items related to each item. You can use something like PCA, but SVD is just as good here due to near zero mean. You could SSVD or ALS from Mahout to do this analysis and then use k-means on the right singular vectors (aka item representation). What is the high level goal that you are trying to solve with this clustering? On Mon, May 6, 2013 at 12:01 PM, Dominik Hübner cont...@dhuebner.com wrote: And running the clustering on the cooccurrence matrix or doing PCA by removing eigenvalues/vectors? On May 6, 2013, at 8:52 PM, Ted Dunning ted.dunn...@gmail.com wrote: On Mon, May 6, 2013 at 11:29 AM, Dominik Hübner cont...@dhuebner.com wrote: Oh, and I forgot how the views and sales are used to build product vectors. As of now, I implemented binary vectors, vectors counting the number of views and sales (e.g 1view=1count, 1sale=10counts) and ordinary vectors ( view = 1, sale=5). I would recommend just putting the view and sale in different columns and doing cooccurrence analysis on this.
Re: Clustering product views and sales
Hi! As a starting point I remember this conversation containing both elements (although the reconstruction part is rather small, hint!) http://markmail.org/message/5cfewal3oyt6vw2k On Tue, May 7, 2013 at 1:00 AM, Dominik Hübner cont...@dhuebner.com wrote: One more thing for now @Ted: What do you refer to with sparsification and reconstruction? On May 7, 2013, at 12:19 AM, Ted Dunning ted.dunn...@gmail.com wrote: Truly cold start is best handled by recommending the most popular items. If you know *anything* at all such as geo or browser or OS, then you can use that to recommend using conventional techniques (that is, you can recommend for the characteristics rather than for the person). Within a very few interactions, however, real recommendations will kick in. My lately preferred approach is to derive indicators using sparsification or ALS+reconstruction. These indicators can be historical items or static items such as geo information. These indicators can be combined in a single step using a search engine. On Mon, May 6, 2013 at 2:58 PM, Dominik Hübner cont...@dhuebner.com wrote: The cluster was mostly intended for tackling the cold start problem for new users. I want to build a recommender based on existing components or to be precise a combination of them. Unfortunately, the only product meta-data I currently have is the product price. Furthermore, this is a project I am working on alone. As a consequence, the approaches I can examine in the given time are limited. Would using ALS and ranking its outcome by e.g. frequent item set algorithms be something worth looking into? Or did you mean something different? My personal goal is to build a recommender providing acceptable results using the data I currently have available. Of course, this will only serve as a basis for further improvements where necessary or if further information can be obtained. On May 6, 2013, at 11:21 PM, Ted Dunning ted.dunn...@gmail.com wrote: Are you looking to build a product recommender based on your own design? Or do you want to build one based on existing methods? If you want to use existing methods, clustering has essentially no role. I think that composite approaches that use item meta-data and different kinds of behavioral cues are important to best performance. On Mon, May 6, 2013 at 12:35 PM, Dominik Hübner cont...@dhuebner.com wrote: Well, as you already might have guessed, I am building a product recommender system for my thesis. I am planning to evaluate ALS (both, implicit and explicit) as well as item -similarity recommendation for users with at least a few known products. Nevertheless, the majority of users only has seen a single (or 2-3) product(s). I want to recommend them the most popular items from clusters, their only product comes from (as a workaround for the cold-start problem). Furthermore, I expect to be able to see which kind of products users like. This might provide me some information about how well ALS and similarity recommenders fit the user's area of interest (an early evaluation) or at least to estimate if the chosen approach will work in some way. On May 6, 2013, at 9:09 PM, Ted Dunning ted.dunn...@gmail.com wrote: I don't even think that clustering is all that necessary. The reduced cooccurrence matrix will give you items related to each item. You can use something like PCA, but SVD is just as good here due to near zero mean. You could SSVD or ALS from Mahout to do this analysis and then use k-means on the right singular vectors (aka item representation). What is the high level goal that you are trying to solve with this clustering? On Mon, May 6, 2013 at 12:01 PM, Dominik Hübner cont...@dhuebner.com wrote: And running the clustering on the cooccurrence matrix or doing PCA by removing eigenvalues/vectors? On May 6, 2013, at 8:52 PM, Ted Dunning ted.dunn...@gmail.com wrote: On Mon, May 6, 2013 at 11:29 AM, Dominik Hübner cont...@dhuebner.com wrote: Oh, and I forgot how the views and sales are used to build product vectors. As of now, I implemented binary vectors, vectors counting the number of views and sales (e.g 1view=1count, 1sale=10counts) and ordinary vectors ( view = 1, sale=5). I would recommend just putting the view and sale in different columns and doing cooccurrence analysis on this.
