recommendation-engine

问题 I'm planning to implement a recommendation engine, of which details are given here. 'Python' is my preferred choice along with 'neo4j' Database. Can anyone please point out, how integration of 'neo4j' with any web framework like 'django' can be done?. Is it possible to integrate them just like 'PHP'integrates with 'MySQL'? . Thanks in advance.. 回答1: I dont see why not. You can integrate this with Django & serve requests through it... Modeling Categories in Graph Database Neo4J shop categories

问题 I'm in the process of designing a website that is built around the concept of recommending various items to users based on their tastes. (i.e. items they've rated, items added to their favorites list, etc.) Some examples of this are Amazon, Movielens, and Netflix. Now, my problem is, I'm not sure where to start in regards to the mathematical part of this system. I'm willing to learn the math that's required, it's just I don't know what type of math is required. I've looked at a few of the

问题 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

问题 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

问题 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 回答1: 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

问题 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.) 回答1: This is such a

问题 This is my action_slippers Datalist. Please note that this just the part of it: X_id cd iios ui w 1 56548c6ab65dd425cc3dda13 2015-11-24T16:12:26.572Z 194635691 563734c3b65dd40e340eaa56 0.010 2 56548df4b84c321fe4cdfb91 2015-11-24T16:19:00.798Z 194153563 56548df4b84c321fe4cdfb8f 0.010 3 56548fc7735e782a88591662 2015-11-24T16:26:46.952Z 177382028 563e12657d4c410c5832579c 0.010 4 565494e1b84c321fe4ce2f44 2015-11-24T16:48:33.828Z 177382031 563e12657d4c410c5832579c 0.010 5 5654994a735e782a88595802

问题 I have simple Python app. take ratings.csv which has user_id, product_id, rating which contains 4 M record then I use Spark AlS and save the model, then I load it to matrixFactorization. my problem with method predicts which takes more than one second to predict the rating between user and product. my server is 32 G and 8 cores. any suggestion how I can enhance the prediction time to be less than 100milisecond. and what the relationship between a number of records in the data set and the

问题 I'm building a social site for musicians. I would like to take the whole list of songs from the database and rank each one based on its relevancy to the logged in user. One reason for this is that I'd like there to always be 10 songs recommended, even if the user signed up 45 seconds ago. The factors I'm using are: The band members of songs (all would be members of the site, might have all quit the song) The logged in user's member connections (may be none) The most recent update in the song

问题 I am working on a recommendation engine and one problem I am facing right now is the similarity matrix of items are huge. I calculated similarity matrix of 20,000 items and stored them a a binary file which tuned out to be nearly 1 GB. I think it is too big. what is the best way do deal with similarity matrix if you have that many items? Any advice! 回答1: In fact similarity matrix is about how object similar to another objects. Each row consist of neighbors of object(row id), but you don't