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Improving your user experience by suggesting relevant content is a great way to increase engagement. Recommender systems are a collection of algorithms used to suggest items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases, and job finders.

They are broadly classified into Content-Based Filtering and Collaborative Filtering(CF). Content-based filtering methods are based on a description of the item and a profile of the user’s preferences. In other words, these algorithms try to recommend items that are similar to those that a user liked in the past (or is examining in the present). In particular, various candidate items are compared with items previously rated by the user and the best-matching items are recommended. Collaborative Filtering (CF) is based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. CF can be divided into Memory-Based Collaborative Filtering and Model-Based Collaborative filtering.

Popular New Releases in Recommender System

Recommenders 1.1.0

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recommenders

Recommenders 1.1.0

gorse

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DeepCTR

v0.9.0

lightfm

implicit

v0.5.0

Popular Libraries in Recommender System

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1

8 Libraries

401

2

7 Libraries

2482

3

6 Libraries

733

4

6 Libraries

608

5

5 Libraries

142

6

5 Libraries

1073

7

5 Libraries

1414

8

5 Libraries

303

9

5 Libraries

2820

10

4 Libraries

53

Trending Kits in Recommender System

Trending Discussions on Recommender System

    Dataframe users who did not purchase item for user-item collaborative filtering
    How to Deploy ML Recommender System on AWS
    What does .nonzero()[0] mean when we want to compute the sparsity of a matrix?
    how to make an integer index corresponding to a string value?
    How can I ensure that all users and all items appear in the training set of my recommender system?
    LensKit Recommender only returns results for some users, otherwise returns empty DataFrame. Why is this happening?
    How to get similarity score for unseen documents using Gensim Doc2Vec model?
    Unable to create dataframe from RDD
    Combining output in pandas?
    How to get a while loop to start over after error?

QUESTION

Dataframe users who did not purchase item for user-item collaborative filtering

Asked 2022-Mar-05 at 12:35

I intend to use a hybrid user-item collaborative filtering to build a Top-N recommender system with TensorFlow Keras

currently my dataframe consist of |user_id|article_id|purchase

user article purchases

purchase is always TRUE because the dataset is a history of user - article purchases

This dataset has 800,000 rows and 3 columns

2 Questions

  1. How do I process it such that I will have 20% purchase = true and 80% purchase = false to train the model?

  2. Is a 20%, 80% true:false ratio good for this use case?

ANSWER

Answered 2022-Mar-05 at 12:35
  1. How do I process it such that I will have 20% purchase = true and 80% purchase = false to train the model?

Since you only have True values, it means that you'll have to generate the False values. The only False that you know of are the user-item interactions that are not present in your table. If your known interactions can be represented as a sparse matrix (meaning, a low percentage of the possible interactions, N_ITEMS x N_USER, is present) then you can do this:

  1. Generate a random user-item combination
  2. If the user-item interaction exists, means is True, then repeat step 1.
  3. If the user-item interaction does not exist, you can consider it a False interaction.

Now, to complete your 20%/80% part, just define the size N of the sample that you'll take from your ground truth data (True values) and take 4*N False values using the previous steps. Remember to keep some ground truth values for your test and evaluation steps.

  1. Is a 20%, 80% true:false ratio good for this use case?

In this case, since you only have True values in your ground truth dataset, I think the best you can do is to try out different ratios. Your real world data only contains True values, but you could also generate all of the False values. The important part to consider is that some of the values that you'll consider False while training might actually be True values in your test and validation data. Just don't use all of your ground truth data, and don't generate an important portion of the possible combinations.

I think a good start could be 50/50, then try 60/40 and so on. Evaluate using multiple metrics, see how are they changing according to the proportion of True/False values (some proportions might be better to reach higher true positive rates, other will perform worse, etc). In the end, you'll have to select one model and one training procedure according to the metrics that matter the most to you.

Source https://stackoverflow.com/questions/71359291

Community Discussions contain sources that include Stack Exchange Network

    Dataframe users who did not purchase item for user-item collaborative filtering
    How to Deploy ML Recommender System on AWS
    What does .nonzero()[0] mean when we want to compute the sparsity of a matrix?
    how to make an integer index corresponding to a string value?
    How can I ensure that all users and all items appear in the training set of my recommender system?
    LensKit Recommender only returns results for some users, otherwise returns empty DataFrame. Why is this happening?
    How to get similarity score for unseen documents using Gensim Doc2Vec model?
    Unable to create dataframe from RDD
    Combining output in pandas?
    How to get a while loop to start over after error?

QUESTION

Dataframe users who did not purchase item for user-item collaborative filtering

Asked 2022-Mar-05 at 12:35

I intend to use a hybrid user-item collaborative filtering to build a Top-N recommender system with TensorFlow Keras

currently my dataframe consist of |user_id|article_id|purchase

user article purchases

purchase is always TRUE because the dataset is a history of user - article purchases

This dataset has 800,000 rows and 3 columns

2 Questions

  1. How do I process it such that I will have 20% purchase = true and 80% purchase = false to train the model?

  2. Is a 20%, 80% true:false ratio good for this use case?

ANSWER

Answered 2022-Mar-05 at 12:35
  1. How do I process it such that I will have 20% purchase = true and 80% purchase = false to train the model?

Since you only have True values, it means that you'll have to generate the False values. The only False that you know of are the user-item interactions that are not present in your table. If your known interactions can be represented as a sparse matrix (meaning, a low percentage of the possible interactions, N_ITEMS x N_USER, is present) then you can do this:

  1. Generate a random user-item combination
  2. If the user-item interaction exists, means is True, then repeat step 1.
  3. If the user-item interaction does not exist, you can consider it a False interaction.

Now, to complete your 20%/80% part, just define the size N of the sample that you'll take from your ground truth data (True values) and take 4*N False values using the previous steps. Remember to keep some ground truth values for your test and evaluation steps.

  1. Is a 20%, 80% true:false ratio good for this use case?

In this case, since you only have True values in your ground truth dataset, I think the best you can do is to try out different ratios. Your real world data only contains True values, but you could also generate all of the False values. The important part to consider is that some of the values that you'll consider False while training might actually be True values in your test and validation data. Just don't use all of your ground truth data, and don't generate an important portion of the possible combinations.

I think a good start could be 50/50, then try 60/40 and so on. Evaluate using multiple metrics, see how are they changing according to the proportion of True/False values (some proportions might be better to reach higher true positive rates, other will perform worse, etc). In the end, you'll have to select one model and one training procedure according to the metrics that matter the most to you.

Source https://stackoverflow.com/questions/71359291