Questions tagged [recommender-system]

Everything related to recommender systems

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1answer
147 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 ...
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0answers
483 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 ...
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3answers
196 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 ...
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2answers
163 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
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1answer
151 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: ...
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0answers
85 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 ...
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0answers
64 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
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1answer
742 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
945 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. ...
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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
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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
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3answers
190 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 ...
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1answer
5k 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 ...
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2answers
370 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 ...
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0answers
84 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/...
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2answers
609 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 ...
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1answer
451 views

Recommendation for boolean dataset with apache mahout

I was trying to implement Item based Recommender System with the boolean dataset, Dataset example: ...
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0answers
351 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 ...
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0answers
235 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?
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1answer
963 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.
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2answers
341 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
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1answer
618 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
198 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
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1answer
643 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 ...
3
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1answer
313 views

Performance metric in recommender systems with implicit feedback

This paper describes a technique for making recommendations when the feedback is implicit, that is, $r_{ui}$ is only a guess. The recommendation problems boils down to the following optimisation ...
3
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1answer
629 views

Content based recommendation on Mahout

Is it possible to get recommendation on similar product using Mahout ? eg : I have data set of movies with following attributes Movie_name, Actor_1, Actor_2, Actress_1, Actress_2, Director, Theme, ...
4
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3answers
84 views

Make use of relationships on recommendation systems

I have a data set of user rating for movie as user_name, product_name, user_rating and I am using this data to recommend new movie to user (collaborative ...
9
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2answers
841 views

How to model user's buying behavior on Amazon?

For our final course project in Data Science, we proposed the following- Give the Amazon Reviews Dataset, we plan to come up with an algorithm (thats roughly based on Personalized PageRank) that ...
3
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1answer
79 views

Discovering non-interesting attributes

I would like to ask a question about recommender systems. We are showing some movies to users and they have to decide if they like them or not. These movies have only a few attributes Title Director ...
2
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1answer
197 views

Visualizing Latent Features

In performing ALS and getting an item matrix of latent features, what would be the best method for inferring the possible "meaning" of each latent factor in the item space? And as a corollary, are the ...
4
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2answers
5k views

Item Based Collaborative Filtering with No Ratings

I am building a recommender for web pages. For each web page in our data set, we wish to generate a list of web pages that other users have also visited. Our data only shows that a user has either ...
2
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1answer
714 views

Correctly interpreting Cosine Angular Distance Similarity & Euclidean Distance Similarity

As an example, let's say I have a very simple data set. I am given a csv with three columns, user_id, book_id, rating. The rating can be any number 0-5, where 0 means the user has NOT rated the book. ...
3
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1answer
1k views

Which accuracy metric of a ML classifier can maximize map@K of a recommender system for an unbalanced dataset?

I have to build a recommender system & it will be evaluated using map@10 criteria. I have rolled up the data/rows at user-item level & is using Gradient Boosting in scikit learn to build the ...
6
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1answer
3k views

Bechmark for Movielens

I'm looking for a place to find benchmarks against which to evaluate performance on public datasets. In this instance, I'm interested in results on the MovieLens10M dataset. It seems to be ...
8
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1answer
5k views

How to split train/test in recommender systems

I am working with the MovieLens10M dataset, predicting user ratings. If I want to fairly evaluate my algorithm, how should I split my training v. test data? By default, I believe the data is split ...
1
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2answers
2k views

User-based nearest neighbour implementation in R?

I am just starting to learn to use R and am not sure how to find the best packages yet. I am looking for a package that will allow me to calculate user-based nearest neighbours as an input for a ...
5
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1answer
4k views

Item-Item similarity based on text

We're build an item-item recommender based on the text descriptions of the items. Our initial approach was to calculate the TF-IDF vectors for each item. We used a hashing tf with 5000 possible hashes ...
4
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2answers
146 views

Price optimization for tiered and seasonal products

Assuming I can collect the demand of the purchase of a certain product that are of different market tiers. Example: Product A is low end goods. Product B is another low end goods. Product C and D are ...
5
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3answers
800 views

Recommendation - item based vs user based [closed]

I have one clarification - First the definitions- User-based: Recommend items by finding similar users. This is often harder to scale because of the dynamic nature of users. Item-based: Calculate ...
2
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2answers
180 views

In a SVD with user/video bias, why is the UV contribute so small?

I'm testing a SVD-based collaborative filter on my data set, in which the label, $r_{ij}$, is a real value from 0 to 1. Like the many papers suggested, to have a better performance, instead of using $...
1
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1answer
277 views

Matrix factorization for like/dislike/unknown data

Most literature focus on either explicit rating data or implicit (like/unknown) data. Are there any good publications to handle like/dislike/unknown data? That is, in the data matrix there are three ...
5
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2answers
780 views

Item based recommender using SVD

I have an item-item similarity matrix. e.g. (the matrix is symmetric, and much bigger): ...
3
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2answers
101 views

What kind of data is not appropriate using CF to do recommendation?

I am currently working on a recommendation system for daily news. At first, I evaluated all the recommender algorithms and their corresponding settings (e.g., similarities, factorizers, ...etc) ...
1
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1answer
75 views

Mimic a Mahout like system

I have a data set, in excel format, with account names, reported symptoms, a determined root cause and a date in month year format for each row. I am trying to implement a mahout like system with a ...
2
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1answer
210 views

Which Graph Properties are Useful for Predictive Analytics?

Let's assume I'm building a content recommendation engine for online content. I have web log data which I can import into a graph, containing a user ID, the page they viewed, and a timestamp for when ...
14
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2answers
25k views

Item based and user based recommendation difference in Mahout

I would like to know how exactly mahout user based and item based recommendation differ from each other. It defines that User-based: Recommend items by finding similar users. This is often harder to ...
4
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1answer
2k views

Mahout Similarity algorithm comparison

Which of the following is best (or widely used) for calculating item-item similarity measure in mahout and why ? ...
1
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2answers
256 views

Graphlab vs Mahout [closed]

I have some question regarding to the choice of the better implementation. I would know the differences and advantages of Mahout Apache (Java implementation) versus Graphlab (Python implementation) in ...
6
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1answer
182 views

Evaluating Recommendation engines

What is the standard way for evaluating and comparing different algorithms while developing recommendation system? Whether we need to have a predetermined annotated ranked dataset and then compare ...
2
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0answers
290 views

Creating Data model for mahout recommendation engine

I am trying to build an item-item similarity matching recommendation engine with mahout. The data set is as in the following format ( attributes are in text not in numerals format ) ...