Questions tagged [recommender-system]

Everything related to recommender systems

Filter by
Sorted by
Tagged with
2
votes
1answer
3k 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 ...
9
votes
2answers
930 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 ...
1
vote
1answer
184 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
vote
0answers
583 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 ...
5
votes
2answers
7k 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 ...
1
vote
1answer
173 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: ...
2
votes
2answers
444 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. ...
3
votes
0answers
98 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 ...
2
votes
1answer
904 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
votes
0answers
1k 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
3answers
260 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 ...
10
votes
1answer
8k 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
562 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
votes
0answers
98 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
893 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 ...
2
votes
0answers
399 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 ...
0
votes
1answer
983 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.
2
votes
1answer
287 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 ...
3
votes
2answers
346 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 ...
3
votes
1answer
710 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, ...
2
votes
0answers
202 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
848 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 ...
18
votes
3answers
285 views

Does click frequency account for relevance?

While building a rank, say for a search engine, or a recommendation system, is it valid to rely on click frequency to determine the relevance of an entry?
1
vote
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 ...
3
votes
1answer
82 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 ...
6
votes
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 ...
2
votes
1answer
881 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. ...
6
votes
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 ...
3
votes
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 ...
2
votes
1answer
217 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 ...
3
votes
2answers
113 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
vote
1answer
322 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
votes
1answer
172 views

Data scheduling for recommender

I do at the moment some data experiments with the Graphlab toolkit. I have at the first next SFrame, with the three columns: Users Items Rating The pair in the ...
2
votes
2answers
187 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 $...
5
votes
3answers
977 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 ...
5
votes
2answers
876 views

Item based recommender using SVD

I have an item-item similarity matrix. e.g. (the matrix is symmetric, and much bigger): ...
3
votes
1answer
102 views

Trouble representing a problem

I have a problem and I'm having trouble representing it - first I thought I should use graph theory (nodes and edges) and now I'm not sure. My data is some tanks names and it's volumes, those tanks ...
2
votes
2answers
276 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
votes
3answers
156 views

Can we quantify how position within search results is related to click-through probability?

Suppose, for example, that the first search result on a page of Google search results is swapped with the second result. How much would this change the click-through probabilities of the two results? ...
5
votes
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 ? ...
8
votes
1answer
220 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
votes
0answers
323 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 ) ...
6
votes
2answers
8k views

Cosine Similarity for Ratings Recommendations? Why use it?

Lets say I have a database of users who rate different products on a scale of 1-5. Our recommendation engine recommends products to users based on the preferences of other users who are highly similar....
1
vote
2answers
887 views

Recommended Language/Framework for Building a New Recommendation Engine

Next week I'm going to begin prototyping a recommendation engine for work. I've implemented/completed the Netflix Challenge in Java before (for college) but have no real idea what to use for a ...
9
votes
2answers
3k views

Difference between using RMSE and nDCG to evaluate Recommender Systems

What kind of error measures do RMSE and nDCG give while evaluating a recommender system, and how do I know when to use one over the other? If you could give an example of when to use each, that would ...
2
votes
0answers
46 views

Modeling Pipeline Budget

I have been tasked with creating a pipeline chart with the live data and the budgeted numbers. I know what probability of each phase of reaching the next. The problem is I have no Idea what to do ...
4
votes
1answer
2k views

Database for a trie, or other appropriate structure for recommendation engine

We are storing the information about our users showing interest in our items. Based on this information, we would like to create a simple recommendation engine that will take the items I1, I2, I3 etc ...
7
votes
2answers
3k views

Create most “average” cosine similarity observation

For a recommendation system I'm using cosine similarity to compute similarities between items. However, for items with small amounts of data I'd like to bin them under a general "average" category (in ...
6
votes
1answer
2k views

How to normalize results of Singular Value Decomposition (SVD) between 0 and 1?

I'm building a recommender system and using SVD as one of the preprocessing techniques. However, I want to normalize all my preprocessed data between 0 and 1 because all of my similarity measures (...
10
votes
1answer
1k views

How should one deal with implicit data in recommendation

A recommendation system keeps a log of what recommendations have been made to a particular user and whether that user accepts the recommendation. It's like ...

1
4 5 6 7
8