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Questions tagged [recommender-system]

Everything related to recommender systems

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votes
1answer
156 views

POC - Get an idea to create a Predictive Model

I'm trying to look for an idea to create a predictive model having the following data: ...
0
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1answer
2k views

Recommendation System to integrate with an android app [closed]

I need to build a recommendation system that takes certain parameters as input, computes a score and order suggestions to users based on this score. Well this is what I need to do loosely speaking. I ...
2
votes
2answers
764 views

Job Recommendation Engine

My girlfriend has recently been struggling with finding a new job, so I thought I'd make a website to help her out. The basic idea is that she'll be shown a list of jobs, rate her interest, and then a ...
4
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2answers
3k views

SVD for recommendation engine

I'm trying to build a toy recommendation engine to wrap my mind around Singular Value Decomposition (SVD). I've read enough content to understand the motivations and intuition behind the actual ...
2
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2answers
228 views

Which recommender system approach allows for inclusion of user profile?

I wanted to enhance a recommendation engine with information relying not only on past purchases or ratings but also on behavioral and demographical variables like sex, age, location, service usage ...
4
votes
1answer
169 views

Recommendation/personalization algorithm conflict

I'm trying to build a recommendation engine for an e-commerce site. By using the common recommendation approach, I'm assuming that each product I recommend has the same value, so all I need to do is ...
3
votes
1answer
383 views

How to handle data collecting bias in machine model training

In many ML problems we collect data and train models using the collected data. Using recommendation as an example, data collected could be biased for various reasons: presentation bias. For example, ...
3
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5answers
3k views

Machine learning algorithm for ranking

I am working on a ranking question, recommending k out of m items to the users. The evaluation metric is average precision at K. Both R and Python have xgboost can be used for pairwise comparison ...
2
votes
2answers
4k views

spark item similarity recommendation

I would like to build an recommendation engine using spark's Mlib itemsimilarity as mentioned here LINK But it seems spark do not have this algorithm any more and ...
10
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1answer
3k views

Spark ALS: recommending for new users

The question How do I predict the rating for a new user in an ALS model trained in Spark? (New = not seen during training time) The problem I'm following the official Spark ALS tutorial here: ...
2
votes
1answer
499 views

Which algorithms should I use for recommendation system using a graph database?

Basically I'm developing a recommendation system using a graph database (specifically neo4j), and I want to apply recommendation algorithms. Since i'm using a graph database, I can see the ...
2
votes
1answer
782 views

how to evaluate top n recommendation system with movie lens dataset?

Based on my research a recommendation system are a subclass of information filtering system that seek to predict the "rating" or "preference" that a user would give to an item. And I'm currently ...
4
votes
4answers
269 views

How can conclusions be drawn from recommendation systems evaluation?

From my research, a recommendation system are a subclass of information filtering system that seek to predict the "rating" or "preference" that a user would give to an item. And basically exists many ...
3
votes
1answer
558 views

Using AWS ML to recommend products

I have millions of user ratings on about 2k products. I want to use Machine Learning to analyse these ratings and recommend products to users based on other users ratings of the same and different ...
1
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3answers
985 views

How to use ML to forecast sales of a brand new product

I am working on a forecasting problem and came across this issue. How do I forecast sales of a brand new product? For example, a product has been introduced in the store and the store would like to ...
2
votes
0answers
328 views

wrong prediction from graphlab.recommender.item_similarity_recommender

I have a question about basic understanding of how item-item collaborative filtering of "Graphlab" library works. I run this code: ...
3
votes
3answers
812 views

Solution for in Time/Space Complexity challenge in Recommendation System?

I have a book Recommendation System project and have a huge data set of feature vectors. What is the best solution for in memory computation? I mean, the program should: calculate the cosine ...
1
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0answers
194 views

KNN on collaborative filtering

After I calculated the similarities matrix, how do I get the neighbors, for example, consider the matrix of similarities between users, if I did not make any mistakes, the matrix must be symmetric ...
2
votes
2answers
6k views

Clustering users based on buying behaviour

I have a set of data which indicates purchase transaction of users (~1 million records). User can have more than 1 purchase across time. Data is spread over 6-7 months. Attributes that I have are ...
2
votes
1answer
447 views

recommendation system for eCommerce healthcare portal suggestion

I am trying to build a recommendation system. My system is basically a ecommerce application where our customers answers a bunch of questions related to healthcare (their basic health related question)...
2
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0answers
338 views

Taxonomy of recommender system methodologies

There's tons of material online but yet I can't reconcile the different definitions for recommender system methodologies / strategies. I think we can identify several axes: memory vs model based; ...
4
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1answer
932 views

Recommendations and Missing Data in Deep Learning

In this research paper, it is discussed how to combine deep learning with wide (shallow) learning to achieve both generalisation and the ability to learn correlation/association rules. The input ...
1
vote
1answer
2k views

Multiclass Classification with large number of categories

I am making a recommendation system (kind of) and I have to recommend the item a user is most likely to buy in his next purchase. Doesn't matter if he already bought this item. Given this, I'm ...
2
votes
1answer
298 views

Vectorizing equation in MATLAB

I am working on collaborative filtering using matrix factorization in MATLAB. I am using Gradient Descent for parameter learning. The cost function to optimize is : $ J = {\left\| I \odot (R - U V') \...
7
votes
2answers
3k views

Which supervised learning algorithms are available for matching?

I'm working on a non-profit where we try to help potential university applicants by matching them with alumni that want to share their experience/wisdom and, at the moment, it is happening manually. ...
8
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2answers
320 views

What should be the value of non-rated field when finding cosine similarity

I am working on a very basic book recommender system. I want to know what to do with the fields which aren't rated by the user when finding cosine similarity, should we ignore them and calculate only ...
1
vote
1answer
154 views

Match users based on the content of their articles

I have users in my database that I would like to match up or group togetter based on the content of there articles. I cant seem to find how this kind of problem is being solved today. Any advice will ...
1
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0answers
499 views

Spark MLlib recommendation - restaurant/ item similarity - issues/improvement

Use Case Recommend similar items (restaurants) to diner. Solution: We have used apache spark MLlib ALS algorithm. Values for lambda and rank has been obtained by iterating through all permutations ...
9
votes
3answers
201 views

What recommendation engine for a situation where users can only see a fraction of all items?

I want to add a recommendation feature to a document management system. It is a server on which most company documents are stored. Employees browse the web interface and click to download (or read ...
2
votes
2answers
197 views

Recommendation model that can recommend already bought item

Most recommendation algorithms recommend new products to users. If you bought this you might like that But sometimes the item user is most likely to buy is an item that he bought sometime ago. ...
1
vote
1answer
159 views

Unknown program 'spark-itemsimilarity' chosen

I have cloudera CDH5 running inside a virtual box. when I try to run : mahout spark-itemsimilarity .... I get the error: ...
3
votes
0answers
90 views

Interpretation of Similarity Number generated by LogLikehood in Mahout

I have a pretty basic question and I was hoping someone could help me. I’m not a math person and I’m fairly new to mahout so I’m looking for a poor’s man explanation. It is a typical order ...
1
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0answers
67 views

collaborative filtering in mahout

How the recommendation results using collaborative filtering depends on the type of data? As in collaborative filtering we just need user id, item id and rating then how it will differ in regard to ...
2
votes
1answer
768 views

How to prepare the training data for SVD-based Recommendation?

I am trying to build an SVD-based recommender system. According to my understanding, the training data should only contain the users who buy at least m items and the items which are bought by n unique ...
2
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0answers
998 views

How to use Python's FastFM library (factorization machines) for recommendation tasks?

I have a dataset of <user, item> pairs where each entry records which user bought which item. e.g. ...
1
vote
1answer
1k views

Implicit Training Models in Spark MLlib?

I have something of a ground floor question I’d like to ask. I’m looking at various options for recommendation engines using Spark. I feel that I have a decent grasp of the basics of collaborative ...
9
votes
2answers
4k views

Benchmark datasets for collaborative filtering

I'd like to test a new algorithm for collaborative filtering. A typical use case is to recommend movies based on the preferences of users similar to the specific user. What are some common benchmark ...
1
vote
3answers
194 views

Algorithms to generate a rating system based on history

I am a beginner in data science. I have a data set of drivers that has the following attributes available- Time stamp Speed Acceleration GPS co-ordinates I need to build a driver rating system to ...
7
votes
1answer
6k views

How do you calculate how dense or sparse a dataset is?

I'm looking deeper into collaborative filtering. One really interesting paper is "A Comparative Study of Collaborative Filtering Algorithms" http://arxiv.org/pdf/1205.3193.pdf In order to select ...
1
vote
2answers
401 views

Collaborative filtering when multiple items are rated multiple times by same user

When trying to model as a recommendation problem the selection of an item that can be selected (and rated) by the same user many times, I can't find references of previous work. For example User1 ...
2
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0answers
85 views

Selecting the number of hashes for minhash? Working with extremely sparse data and want more collisions

I'm attempting to use minhash to generate clusters and similarities, and I am primarily using ideas from these resources. http://www2007.org/papers/paper570.pdf https://chrisjmccormick.wordpress.com/...
1
vote
2answers
646 views

Can i find similar players using a clustering method like the k-mean algorithm?

I am working on a data mining project on NBA data. I want to make a recommendation system similar to the google one, where you search for players and you get recommendation for similar players. I ...
0
votes
1answer
465 views

Recommendation for boolean dataset with apache mahout

I was trying to implement Item based Recommender System with the boolean dataset, Dataset example: ...
2
votes
0answers
367 views

Spark ALS-WR giving the same recommended items for all users

We are trying to build a recommendation system for a supermarket with diverse item types (ranging from fast-moving grocery to low-moving electronic items). Some items are purchased more frequently in ...
1
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0answers
242 views

Content based recommendations using SQL queries on the MovieLens data set

how can I generate content based recommendations using SQL queries on the MovieLens data set?
0
votes
1answer
967 views

using movielens dataset build recommendation engine [closed]

Where I can get the complete guide (step by step )on building a recommender system for example using movielens datsets building content based, collaborative or may be hybrid system.
3
votes
2answers
343 views

Deep Learning for Recommender System

I read about Recursive Neural Networks that they can convert Documents to distributed word representation. In the context of new article recommendation, I am thinking to use this model to convert ...
4
votes
1answer
703 views

How to deal with a sparse matrix when using a perceptron based recommender system?

I'm constrained to use a perceptron based method. I have a user-item matrix filled with rating data on scale of 1 to 5 like this, with around 50% of the matrix with no data: ...
2
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0answers
199 views

How to create Self learning data product

I am trying to build price recommendation solution for clients in a scalable manner. I have two choices as below. Professional service: Statistician involvement to build regression model or any ...
3
votes
1answer
679 views

How to determine Nonnegativity in Matrix Factorization?

We have information about what the user likes in our app and we want to recommend content to similar users even those who may not have explicitly like a particular content but are similar to those who ...