recommendation-engine

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 FLATTEN the output to look like: (A, (1,2)) (A, (1,3)) (A, (1,4)) (A, (2,3)) (A, (2,4)) (A, (3,4)) (B, (1,2))

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/recommenderlabrats/libs/recommenderlabrats.so: undefined symbol: dgels_ Error: loading failed Execution halted ERROR:

I am looking for datasets that can be used for implementing recommendation system usecase of Apache Mahout. I know of only MovieLens Data Sets from GroupLens Research group. Anyone knows any other datasets that can be used for recommendation system implementation? I am particularly interested in item-based data sets though other datasets are most welcome. this is Sebastian from Mahout. There is a dataset from a czech dating website available that might be of interest to you: http://www.occamslab.com/petricek/data/ Btw the term item-based refers to a special collaborative filtering approach not

I have a matrix with 129539 rows and 530 columns. The first column correspond to ClientIDs and the first row to product brands. Inside I have a ranking index that each ClientID has for every product brand (0 if the ClientID never bought the product, all the way up to 10 otherwise). I am building a User Based Collaborative Filtering Recommender System in R, using the first 5000 rows for training, and it gives me an output that doesn't make sense to me. The code I have to generate it is the following: # Loading to pre-computed affinity data affinity.data <-read.csv("mydirectory") affinity.matrix

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 preference scores, where the articles subset has a preference score of 1 (for example) and the

I am new to Spark. I would like to make a sparse matrix a user-id item-id matrix specifically for a recommendation engine. I know how I would do this in python. How does one do this in PySpark? Here is how I would have done it in matrix. The table looks like this now. Session ID| Item ID | Rating 1 2 1 1 3 5 import numpy as np data=df[['session_id','item_id','rating']].values data rows, row_pos = np.unique(data[:, 0], return_inverse=True) cols, col_pos = np.unique(data[:, 1], return_inverse=True) pivot_table = np.zeros((len(rows), len(cols)), dtype=data.dtype) pivot_table[row_pos, col_pos] =