3 answers
kouma
Student
Japan
1
Question
Asked
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Translated from Japanese .
How do you learn recommendation systems?
How do you learn recommendation systems?
hi,I'm trying to create a web application using a hybrid recommendation system, but I don't know how to study it.I'm trying to create a web application using a library called lightfm, which is said to be suitable for creating a hybrid recommendation system. I'm trying to do this, but I'm having trouble finding any articles that explain it in detail. This is my first time learning about machine learning. I've been looking into it on Kaggle, but I don't fully understand the hybrid recommendation system or lightfm yet, so I'm looking for articles and sites that will help me understand the overall picture, as well as sample code to actually implement it. Please give me some advice on studying.
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3 answers
Karin P.
Lecturer
378
Answers
Durban, KwaZulu-Natal, South Africa
Updated
Karin’s Answer
Konnichi-wa Kouma-san,
I am not sure that you will get lot's of answers here. We give career advice to high-school and College students.
You need to find a community of people who speak your (AI) language and can help you with such a specific question. GitHub comes to mind. Or you could just go on Twitter and find the people who are also into AI and machine learning. They will be able to point you to further groups and resources.
Good luck!
KP
I am not sure that you will get lot's of answers here. We give career advice to high-school and College students.
You need to find a community of people who speak your (AI) language and can help you with such a specific question. GitHub comes to mind. Or you could just go on Twitter and find the people who are also into AI and machine learning. They will be able to point you to further groups and resources.
Good luck!
KP
Thank you, Karin for the advice.
kouma
Ganesh Subramaniam
Technology Architect / Senior Manager
9
Answers
Toronto, Ontario, Canada
Updated
Ganesh’s Answer
Hello Kouma,
Just as Karin mentioned, this platform may not be the ideal place to seek in-depth responses to particular queries. However, I've provided a link below that offers a comprehensive understanding of the topic.
https://towardsdatascience.com/recommender-systems-a-complete-guide-to-machine-learning-models-96d3f94ea748
Just as Karin mentioned, this platform may not be the ideal place to seek in-depth responses to particular queries. However, I've provided a link below that offers a comprehensive understanding of the topic.
https://towardsdatascience.com/recommender-systems-a-complete-guide-to-machine-learning-models-96d3f94ea748
I appreciate your support, Ganesh
kouma
Patrick Meyer
Executive - Data Analytics and AI
566
Answers
Naperville, Illinois
Updated
Patrick’s Answer
Kouma, thank you for reaching out. I congratulate you on your pursuit of learning recommendation systems, with a focus on crafting a hybrid recommendation system using the LightFM library. Based on what you have provided, I think you should consider the following:
1. I would first think about establishing a robust foundation in machine learning, grasping concepts such as supervised and unsupervised learning, collaborative filtering, and content-based filtering for a contextual understanding of recommendation systems.
2. You might also want to begin with introductory materials on recommendation systems through platforms like Coursera, edX, and Khan Academy, opting for courses covering both theoretical principles and practical implementation.
3. Another avenue is to thoroughly explore the LightFM documentation to gain a deeper understanding of the library, benefiting from detailed explanations and code examples that serve as valuable resources for your web application development.
4. You should also look into tutorials and articles specifically tailored to implementing hybrid recommendation systems with LightFM. Platforms like Medium, Towards Data Science, and GitHub often host beginner-friendly content with step-by-step guides and sample code.
5. Leverage the Kaggle community for practical insights and support, engaging with discussions, kernels, and datasets related to recommendation systems. Actively participate, ask questions, and learn from the experiences of others.
6. Consider referring to dedicated books on recommendation systems, such as "Recommender Systems" by Jannach and Zanker, offering a comprehensive resource covering various aspects and enhancing your learning journey.
7. Apply your knowledge through practical projects, starting with small implementations and gradually increasing complexity. Building your web application will provide real-world experience, solidifying your understanding of recommendation systems.
8. Network and seek guidance from professionals in the field through forums, LinkedIn, or local meetups. Learning a new field, especially as intricate as machine learning, requires time and persistence. Break down the learning process into manageable steps and celebrate small victories along the way.
1. I would first think about establishing a robust foundation in machine learning, grasping concepts such as supervised and unsupervised learning, collaborative filtering, and content-based filtering for a contextual understanding of recommendation systems.
2. You might also want to begin with introductory materials on recommendation systems through platforms like Coursera, edX, and Khan Academy, opting for courses covering both theoretical principles and practical implementation.
3. Another avenue is to thoroughly explore the LightFM documentation to gain a deeper understanding of the library, benefiting from detailed explanations and code examples that serve as valuable resources for your web application development.
4. You should also look into tutorials and articles specifically tailored to implementing hybrid recommendation systems with LightFM. Platforms like Medium, Towards Data Science, and GitHub often host beginner-friendly content with step-by-step guides and sample code.
5. Leverage the Kaggle community for practical insights and support, engaging with discussions, kernels, and datasets related to recommendation systems. Actively participate, ask questions, and learn from the experiences of others.
6. Consider referring to dedicated books on recommendation systems, such as "Recommender Systems" by Jannach and Zanker, offering a comprehensive resource covering various aspects and enhancing your learning journey.
7. Apply your knowledge through practical projects, starting with small implementations and gradually increasing complexity. Building your web application will provide real-world experience, solidifying your understanding of recommendation systems.
8. Network and seek guidance from professionals in the field through forums, LinkedIn, or local meetups. Learning a new field, especially as intricate as machine learning, requires time and persistence. Break down the learning process into manageable steps and celebrate small victories along the way.