Questions tagged [recommender-system]

Everything related to recommender systems

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17
votes
2answers
8k views

Recommending movies with additional features using collaborative filtering

I am trying to build a recommendation system using collaborative filtering. I have the usual [user, movie, rating] information. I would like to incorporate an ...
2
votes
2answers
7k 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 ...
12
votes
2answers
2k views

Preference Matching Algorithm

There's this side project I'm working on where I need to structure a solution to the following problem. I have two groups of people (clients). Group A intends to ...
7
votes
2answers
6k views

Recommender system based on purchase history, not ratings

I'm exploring options for recommender systems optimized for the insurance industry, which would take into account i) product holdings ii) user characteristics (segment, age, affluence, etc.). I ...
10
votes
1answer
9k 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 ...
4
votes
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 ...
6
votes
1answer
499 views

Understanding Youtube recommender (candidate generation step)

I'm trying to understand Deep Neural Networks for YouTube Recommendations. Their candidate generation step outputs top N items via softmax (with negative sampling) at training time . via ...
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 ...
2
votes
2answers
137 views

Proceeding with various methods for news recommendation

I am beginner in ML (i have done only Andrew Ng's ML course) and i have to work on news recommendation. I went through this paper which mentions different methods used for news recommendation (at 7th ...
2
votes
1answer
549 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
380 views

Calculating Rank Ordering Error Metric for implicit recommendation

I'm reading Collaborative Filtering for Implicit Feedback Datasets. On page 6 they detail their evaluation strategy, which they define as mean Expected Percentile Ranking with the following formula: $...
2
votes
2answers
154 views

Is there any standard pattern recognition algorithm in predicting an item which a user will be buying next, given I have the history of the purchases

I am having a list of 10 different items a user has bought in the past. Each item has been bought multiple times. I would like to find a pattern in which the user buys a particular item and predict ...
5
votes
1answer
173 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 ...
5
votes
1answer
86 views

Evaluating the performance of a machine learned recommendation system

I have a set of resumes $R=\{{r_1,...,r_n\}}$, which I've transformed to a vector space using TF-IDF. Each resume has a label, which is the name of their current employer. Each of these labels comes ...
3
votes
3answers
1k 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 ...
2
votes
1answer
3k views

Updating One-Hot Encoding to account for new categories

My question is focused around how to appropriately update an encoded feature set when a new category is introduced by the test data. I use the data in logistic regression and I know it is not a 'live' ...
1
vote
2answers
69 views

Evaluate document similarity / content-based recommender system

I'm planning on building a basic content-based recommender system with word2vec and cosine similarity. The data consists of 300k documents in varying length. How do I evaluate my model if I have no ...