recommendation-engine

Looking for expertise to guide me on issue below. Background: I'm trying to get going with a basic PySpark script inspired on this example As deploy infrastructure I use a Google Cloud Dataproc Cluster. Cornerstone in my code is the function "recommendProductsForUsers" documented here which gives me back the top X products for all users in the model Issue I incur The ALS.Train script runs smoothly and scales well on GCP (Easily >1mn customers). However, applying the predictions: i.e. using funcitons 'PredictAll' or 'recommendProductsForUsers', does not scale at all. My script runs smooth for a

I am trying to change matrix into a structure that I can use in functions of the recommenderlab package. datafile1 <- as(datafile1,"matrix") datafile1 name1 name2 rating1 rating2 rating3 rating4 rating5 rating6 [1,] "1" "a" "0" "0" "1" "0" "0" "0" [2,] "2" "d" "0" "0" "1" "0" "0" "0" [3,] "3" "x" "1" "0" "1" "0" "0" "0" [4,] "4" "b" "0" "1" "1" "0" "0" "0" library(recommenderlab) datafile1 <- as(datafile1, "realRatingMatrix") This is the result: Error in asMethod(object) : invalid class 'NA' to dup_mMatrix_as_geMatrix Does anyone have an idea about what's going wrong here? The problem is that

I've been playing around with lightfm for quite some time and found it really useful to generate recommendations. However, there are two main questions that I would like to know. to evaluate the LightFM model in case where the rank of the recommendations matter, should I rely more on precision@k or other provided evaluation metrics such as AUC score ? in what cases should I focus on improving my precision@k compared to other metrics? or maybe are they highly correlated? which means if I manage to improve my precision@k score, the other metrics would follow, am I correct? how would you

Does Mahout provide a way to determine similarity between content? I would like to produce content-based recommendations as part of a web application. I know Mahout is good at taking user-ratings matrices and producing recommendations based off of them, but I am not interested in collaborative (ratings-based) recommendations. I want to score how well two pieces of text match and then recommend items that match most closely to text that I store for users in their user profile... I've read Mahout's documentation, and it looks like it facilitates mainly the collaborative (ratings-based)

currently when a new user comes I cannot update my recommender system which apprently is related to not having added the user and item matrix. Where can I find this and how to do this? Thanks model.userFactors model.itemFactors When items features and users features are computed the model is prepared only to recommend for known items and users. If You have new user/item, You have to cope with cold start problem. But there are two things - making recommendations work for new users/items and the separate thing is updating the model (features matrices) near-online. In order to prepare

I've always been curious as to how these systems work. For example, how do netflix or Amazon determine what recommendations to make based on past purchases and/or ratings? Are there any algorithms to read up on? Just so there's no misperceptions here, there's no practical reason for me asking. I'm just asking out of sheer curiosity. (Also, if there's an existing question on this topic, point me to it. "Recommendations system" is a difficult term to search for.) This is such a commercially important application that Netflix introduced a $1 million prize for improving their recommendations by 10