recommendation-engine | 易学教程

Spark ALS recommendation system have value prediction greater than 1

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问题 I'm using ALS algorithm ( implicitPrefs = True )in Spark ( Recommendation system algorithm ). Normally, after run this algorithm, value predict must be from 0 to 1. But i received value greater than 1 "usn" : 72164, "recommendations" : [ { "item_code" : "C1346", "rating" : 0.756096363067627 }, { "item_code" : "C0117", "rating" : 0.966064214706421 }, { "item_code" : "I0009", "rating" : 1.00000607967377 }, { "item_code" : "C0102", "rating" : 0.974934458732605 }, { "item_code" : "I0853", "rating

Mahout: adjusted cosine similarity for item based recommender

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For an assignment I'm supposed to test different types of recommenders, which I have to implement first. I've been looking around for a good library to do that (I had thought about Weka at first) and stumbled upon Mahout. I must therefore put forward that: a) I'm completely new to Mahout b) I do not have a strong background in recommenders nor their algorithms (otherwise I wouldn't be doing this class...) and c) sorry but I'm far from being the best developper in the world ==> I'd appreciate if you could use layman terms (as far as possible...) :) I've been following some tutorials (e.g. this

How do I adapt my recommendation engine to cold starts?

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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). 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 "gender, nationality and so on" will amount to more than stereotyping. IIRC, places such as Amazon built up

Reconstructing new data using sklearn NMF components Vs inverse_transform does not match

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问题 I fit a model using scikit-learn NMF model on my training data. Now I perform an inverse transform of new data using result_1 = model.inverse_transform(model.transform(new_data)) Then I compute the inverse transform of my data manually taking the components from the NMF model, using the equation as in Slide 15 here. temp = np.dot(model.components_, model.components_.T) transform = np.dot(np.dot(model.components_.T, np.linalg.pinv(temp)), model.components_) result_2 = np.dot(new_data,

Multikey Multivalue Non Deterministic python dictionary

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问题 There is already a multi key dict in python and also a multivalued dict. I needed a python dictionary which is both: example: # probabilistically fetch any one of baloon, toy or car d['red','blue','green']== "baloon" or "car" or "toy" Probability of d['red']==d['green'] is high and Probability of d['red']!=d['red'] is low but possible the single output value should be probabilistically determined (fuzzy) based on a rule from keys eg:in above case rule could be if keys have both "red" and

Mahout Plugin for ruby on rails

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问题 I want to use Apache Mahout in my project on Ruby on Rails for implementing recommendations and collaborative filtering. In Particular my requirements are: suggesting related tags. suggesting related articles. based on user's preferences prompt him for review of articles. based on geographical location, and other meta information of a user, suggest him similar users. I am open to using any other solution (other than mahout) if it integrates with rails easily and fulfills my requirements. 回答1:

Design of the recommendation engine database?

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i am currently working on recommendation systems especially for audio files.but i am a beginner at this subject.i am trying to design database first with mysql but i cant decide how to do it.İt is basicly a system which users create profile then search for the music and system recommend them music similar to they liked. which database should i use ?(Mysql comes my mind as a first guess) it is a web project and also then with mobile side.Which technologies should i use?(php,android platform...) what are the pitfalls of this project. how to design database for system like that? Any relational

How to improve my recommendation result? I am using spark ALS implicit

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问题 First, I have some use history of user's app. For example: user1, app1, 3(launch times) user2, app2, 2(launch times) user3, app1, 1(launch times) I have basically two demands: Recommend some app for every user. Recommend similar app for every app. So I use ALS(implicit) of MLLib on spark to implement it. At first, I just use the original data to train the model. The result is terrible. I think it may caused by the range of launch times. And the launch time range from 1 to thousands. So I

Web page recommender system

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问题 I am trying to build a recommender system which would recommend webpages to the user based on his actions(google search, clicks, he can also explicitly rate webpages). To get an idea the way google news does it, it displays news articles from the web on a particular topic. In technical terms that is clustering, but my aim is similar. It will be content based recommendation based on user's action. So my questions are: How can I possibly trawl the internet to find related web-pages? And what

Recommendation Engines for Java applications [closed]

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问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 5 years ago . I was wondering if there is any open source recommendation engine available? It should suggest something like Amazon and Netflix. I have heard of a framework called Apache Mahout - Taste. I am trying it next week. It would be great if you can share your valuable thoughts. 回答1: I'm the developer of Mahout / Taste