kandi background
kandi background
Explore Kits
kandi background
Explore Kits
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

Gorse v0.3.4

v0.9.0

v0.5.0

recommenders

Recommenders 1.1.0

gorse

Gorse v0.3.4

DeepCTR

v0.9.0

lightfm

implicit

v0.5.0

Popular Libraries in Recommender System

recommenders

by microsoft python

star image 12819 MIT

Best Practices on Recommendation Systems

gorse

by zhenghaoz go

star image 5346

An open source recommender system service written in Go

DeepCTR

by shenweichen python

star image 5331 Apache-2.0

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

Surprise

by NicolasHug python

star image 4996 BSD-3-Clause

A Python scikit for building and analyzing recommender systems

lightfm

by lyst python

star image 3668 Apache-2.0

A Python implementation of LightFM, a hybrid recommendation algorithm.

Ad-papers

by wzhe06 python

star image 3287 MIT

Papers on Computational Advertising

librec

by guoguibing java

star image 2959 NOASSERTION

LibRec: A Leading Java Library for Recommender Systems, see

implicit

by benfred python

star image 2731 MIT

Fast Python Collaborative Filtering for Implicit Feedback Datasets

Reco-papers

by wzhe06 python

star image 2521 MIT

Classic papers and resources on recommendation

recommenders

by microsoft python

star image 12819 MIT

Best Practices on Recommendation Systems

gorse

by zhenghaoz go

star image 5346

An open source recommender system service written in Go

DeepCTR

by shenweichen python

star image 5331 Apache-2.0

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

Surprise

by NicolasHug python

star image 4996 BSD-3-Clause

A Python scikit for building and analyzing recommender systems

lightfm

by lyst python

star image 3668 Apache-2.0

A Python implementation of LightFM, a hybrid recommendation algorithm.

Ad-papers

by wzhe06 python

star image 3287 MIT

Papers on Computational Advertising

librec

by guoguibing java

star image 2959 NOASSERTION

LibRec: A Leading Java Library for Recommender Systems, see

implicit

by benfred python

star image 2731 MIT

Fast Python Collaborative Filtering for Implicit Feedback Datasets

Reco-papers

by wzhe06 python

star image 2521 MIT

Classic papers and resources on recommendation

Trending New libraries in Recommender System

SparrowRecSys

by wzhe06 python

star image 1666 Apache-2.0

A Deep Learning Recommender System

RecBole

by RUCAIBox python

star image 1639 MIT

A unified, comprehensive and efficient recommendation library

recommenders

by tensorflow python

star image 1260 Apache-2.0

TensorFlow Recommenders is a library for building recommender system models using TensorFlow.

DeepMatch

by shenweichen python

star image 1204 Apache-2.0

A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.

Recommender-System-with-TF2.0

by ZiyaoGeng python

star image 779 MIT

Recurrence the recommender paper with Tensorflow2.0

EasyRec

by alibaba python

star image 438 Apache-2.0

A framework for large scale recommendation algorithms.

Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising

by guyulongcs python

star image 366

Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR prediction, CVR prediction), Post Ranking, Transfer, Reinforcement Learning, Self-supervised Learning and so on.

KGAT-pytorch

by LunaBlack python

star image 156

Recommend-System-tf2.0

by jc-LeeHub python

star image 156

原理解析及代码实战,推荐算法也可以很简单 🔥 想要系统的学习推荐算法的小伙伴,欢迎 Star 或者 Fork 到自己仓库进行学习🚀 有任何疑问欢迎提 Issues,也可加文末的联系方式向我询问!

SparrowRecSys

by wzhe06 python

star image 1666 Apache-2.0

A Deep Learning Recommender System

RecBole

by RUCAIBox python

star image 1639 MIT

A unified, comprehensive and efficient recommendation library

recommenders

by tensorflow python

star image 1260 Apache-2.0

TensorFlow Recommenders is a library for building recommender system models using TensorFlow.

DeepMatch

by shenweichen python

star image 1204 Apache-2.0

A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.

Recommender-System-with-TF2.0

by ZiyaoGeng python

star image 779 MIT

Recurrence the recommender paper with Tensorflow2.0

EasyRec

by alibaba python

star image 438 Apache-2.0

A framework for large scale recommendation algorithms.

Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising

by guyulongcs python

star image 366

Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR prediction, CVR prediction), Post Ranking, Transfer, Reinforcement Learning, Self-supervised Learning and so on.

KGAT-pytorch

by LunaBlack python

star image 156

Recommend-System-tf2.0

by jc-LeeHub python

star image 156

原理解析及代码实战,推荐算法也可以很简单 🔥 想要系统的学习推荐算法的小伙伴,欢迎 Star 或者 Fork 到自己仓库进行学习🚀 有任何疑问欢迎提 Issues,也可加文末的联系方式向我询问!

Top Authors in Recommender System

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

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

The ability to recommend music, movies, books, and other products is essential to almost all eCommerce businesses. However, this functionality can be complicated to implement. That's why so many eCommerce sites rely on third-party web applications for recommendation functionality instead of building their own. However, this process is often error-prone, time-consuming, and expensive. Instead of spending hours building a recommendation system on your own site, you might consider using an open-source JavaScript recommender system library. You can find dozens of open source libraries on the Internet these days. Many are intended for building social networks or other social APIs, but others work with the media recommendation problem. Below are the top eight JavaScript recommender system libraries in use today.

Go has been an important part of the container ecosystem. It is a key component of Docker, Kubernetes, OpenShift, CoreOS, InfluxDB, and many more tools in the container space. As we move toward a microservices-based architecture and adopt new technologies to help us manage the complexities of these systems, Go’s role is only going to grow. As this growth happens, it will become increasingly important to understand how you can use Go to build scalable microservices that can stand up under pressure. There are many recommender system software available in the market, but finding the best one is difficult. If you are looking for the best recommender system libraries, this kit will provide you with all the necessary information. In this kit, we have listed some of the most popular and powerful Go recommender system libraries which you can use in your next project.

A recommender system, or a recommendation system is a subclass of information filtering systems that are meant to predict the “rating” or “preference” a user would give to an item. They are primarily used in commercial applications. Recommender systems are utilized in a variety of areas and are most commonly recognized as playlist generators for video and music services like Netflix, YouTube, Spotify, and Apple Music. However, there are also more creative uses like for potential friends on dating sites and for professionals or jobs on LinkedIn or AngelList. The following 6 best C++ Recommender System libraries are designed specifically for C++ developers who want to create and run unit tests to ensure their code works correctly. DeepRec - a recommendation engine based on TensorFlow; flipper - Recommendation engine and metainformation server; Movie-Recommendation-Engine - User Based Movie Recommendation System based.

A recommender system intends to predict user preferences based on their past behavior and propose items that may be of interest to them. This can be anything from movies to music and books. Recommendation engines are used everywhere, with the main objective of boosting customer engagement and sales. Java is one of the best programming languages for developing a recommender system. There are many Java libraries that you can use to implement your recommender system in Java. However, each library has its own advantages and disadvantages. Therefore, it is essential to choose the best java recommender system library based on your requirements. The most popular Java Recommender System libraries in 2022: LibrecGUI by T-10001,librec-auto-sample by that-recsys-lab, librecClone by sumitsidana. In this kit, we have listed the 7 best Java libraries for building recommendation systems.

Given a set of users’ preferences and items, recommend a subset of the items to each user in order to maximize the number of preferred items. A recommender system (or recommendation system) is a software application that predicts the “rating” or “preference” that a user would give to an item. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc. if there is any type of information involved. Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general. There are also recommender systems for experts/people and collaborative/content filtering techniques used in these systems. The following is a list of the 8 most popular C# Recommender System libraries in 2022.

The ability to recommend music, movies, books, and other products is essential to almost all eCommerce businesses. However, this functionality can be complicated to implement. That's why so many eCommerce sites rely on third-party web applications for recommendation functionality instead of building their own. However, this process is often error-prone, time-consuming, and expensive. Instead of spending hours building a recommendation system on your own site, you might consider using an open-source JavaScript recommender system library. You can find dozens of open source libraries on the Internet these days. Many are intended for building social networks or other social APIs, but others work with the media recommendation problem. Below are the top eight JavaScript recommender system libraries in use today.

Go has been an important part of the container ecosystem. It is a key component of Docker, Kubernetes, OpenShift, CoreOS, InfluxDB, and many more tools in the container space. As we move toward a microservices-based architecture and adopt new technologies to help us manage the complexities of these systems, Go’s role is only going to grow. As this growth happens, it will become increasingly important to understand how you can use Go to build scalable microservices that can stand up under pressure. There are many recommender system software available in the market, but finding the best one is difficult. If you are looking for the best recommender system libraries, this kit will provide you with all the necessary information. In this kit, we have listed some of the most popular and powerful Go recommender system libraries which you can use in your next project.

A recommender system, or a recommendation system is a subclass of information filtering systems that are meant to predict the “rating” or “preference” a user would give to an item. They are primarily used in commercial applications. Recommender systems are utilized in a variety of areas and are most commonly recognized as playlist generators for video and music services like Netflix, YouTube, Spotify, and Apple Music. However, there are also more creative uses like for potential friends on dating sites and for professionals or jobs on LinkedIn or AngelList. The following 6 best C++ Recommender System libraries are designed specifically for C++ developers who want to create and run unit tests to ensure their code works correctly. DeepRec - a recommendation engine based on TensorFlow; flipper - Recommendation engine and metainformation server; Movie-Recommendation-Engine - User Based Movie Recommendation System based.

A recommender system intends to predict user preferences based on their past behavior and propose items that may be of interest to them. This can be anything from movies to music and books. Recommendation engines are used everywhere, with the main objective of boosting customer engagement and sales. Java is one of the best programming languages for developing a recommender system. There are many Java libraries that you can use to implement your recommender system in Java. However, each library has its own advantages and disadvantages. Therefore, it is essential to choose the best java recommender system library based on your requirements. The most popular Java Recommender System libraries in 2022: LibrecGUI by T-10001,librec-auto-sample by that-recsys-lab, librecClone by sumitsidana. In this kit, we have listed the 7 best Java libraries for building recommendation systems.

Given a set of users’ preferences and items, recommend a subset of the items to each user in order to maximize the number of preferred items. A recommender system (or recommendation system) is a software application that predicts the “rating” or “preference” that a user would give to an item. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc. if there is any type of information involved. Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general. There are also recommender systems for experts/people and collaborative/content filtering techniques used in these systems. The following is a list of the 8 most popular C# Recommender System libraries in 2022.

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

Community Discussions contain sources that include Stack Exchange Network

Tutorials and Learning Resources in Recommender System