recommendation-engine

问题 User-item affinity and recommendations : I am creating a table which suggests "customers who bought this item also bought algorithm " Input dataset productId userId Prod1 a Prod1 b Prod1 c Prod1 d prod2 b prod2 c prod2 a prod2 b prod3 c prod3 a prod3 d prod3 c prod4 a prod4 b prod4 d prod4 a prod5 d prod5 a Output required Product1 Product2 score Prod1 prod3 Prod1 prod4 Prod1 prod5 prod2 Prod1 prod2 prod3 prod2 prod4 prod2 prod5 prod3 Prod1 prod3 prod2 Using code : #Get list of unique items

问题 The Optimization for my Recommender system: min|| R - XY || R: Ratingmatrix X: UserxFaktor Y: FaktorxItem stops working when i increase the size of the input. Just for clarification the code in short: k = 3 n = 10 #5 m= 50 #10 R=np.array(np.arange(n*m)).reshape(n, m) Z0 = np.array(np.random.random(n*k+k*m)) def whatineed (Z): return np.linalg.norm(R - np.dot(Z[:(n*k)].reshape(n,k),Z[(n*k):].reshape(k,m))) def VectorizeX(Matrix): i, j = Matrix.shape return (Matrix.reshape((i * j, 1), order='C'

问题 We have a number of customers that we have to send monthly invoices too. Right now, I'm managing a codebase that does SQL queries against our customer database and billing database and places that data into emails - and sends it. I grow weary of maintaining this every time we want to include a new promotion or change our customer service phone numbers. So, I'm looking for a replacement to move more of this into the hands of those requesting the changes. In my ideal world, I need : A WYSIWYG

问题 This is a typical way of handling sparse features (such as some ID features) in recommendation system. I'm looking for a convenient way to prepare the data for TensorFlow pipeline. I did lots of search, but yet find the good solution yet. Below is the one which seems to be close to what I need, but not working yet. See ####### part below The data file is like: csv = [ '1221,cc,1', '213,aa|cc|ff,1', ] for the second row, i need some SparseTensor like multi-hot aa bb cc dd ee ff | 0 0 1 0 0 0 |

问题 I'd like to try to make a simple twitter client that learns my tastes and automatically finds friends and interesting tweets to provide me with relevant information. To get started, I would need to get a good stream of random twitter messages, so I can test a few machine learning algorithms on them. What API methods should I use for this? Do I have to poll regularly to get messages, or is there a way to get twitter to push messages as they are published? I'd also be interested in learning

问题 I am working on a recommendation engine based on implicit feedback. I was using this link : http://insightdatascience.com/blog/explicit_matrix_factorization.html#movielens This used ALS(Alternating least squares) to compute the user and item vectors. Since, my data set cannot be partitioned by time. I am randomly taking 'x' number of ratings from a user and putting them into the test set. This is a reproducible example of my training user-item matrix. col1 col2 col3 col4 col5 col6 col7 col8

问题 given my input data in userid,itemid format: raw: {userid: bytearray,itemid: bytearray} dump raw; (A,1) (A,2) (A,4) (A,5) (B,2) (B,3) (B,5) (C,1) (C,5) grpd = GROUP raw BY userid; dump grpd; (A,{(A,1),(A,2),(A,4),(A,5)}) (B,{(B,2),(B,3),(B,5)}) (C,{(C,1),(C,5)}) I'd like to generate all of the combinations(order not important) of items within each group. I eventually intend on performing jaccard similarity on the items in my group. ideally my the bigrams would be generated and then I'd

问题 I have a boolean preference recommender based on user similarity. My data set essentially contains relations where ItemId are articles the user has decided to read. I'd like to add a second data model containing where ItemId is a subscription to a particular topic. The only way I can imagine doing this is by merging the two together, offsetting the subscription IDs so that they don't collide with the article IDs. For weighting I considered dropping the boolean preference setup and introducing

问题 I am curious what are the methods / approaches to overcome the "cold start" problem where when a new user or an item enters the system, due to lack of info about this new entity, making recommendation is a problem. I can think of doing some prediction based recommendation (like gender, nationality and so on). 回答1: Maybe there are times you just shouldn't make a recommendation? "Insufficient data" should qualify as one of those times. I just don't see how prediction recommendations based on

问题 I am trying to install the recommenderlabrats package from github to my SUSE Linux R-Server, using the straight forward: devtools::install_github("sanealytics/recommenderlabrats") However I do get an error message which I can't get wrap my head around. Error in dyn.load(file, DLLpath = DLLpath, ...) : unable to load shared object '/home/ruser/R/x86_64-unknown-linux-gnu-library/3.2/recommenderlabrats/libs/recommenderlabrats.so': /home/ruser/R/x86_64-unknown-linux-gnu-library/3.2