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How to build a Personalized News
Recommendation Platform
By Nguyễn Tấn Triều (Thomas)
Contact Email: tantrieuf31@gmail.com...
Agenda
1. Why do we need recommendation systems ?
2. How can we design recommendation systems ?
3. Case study: News Recomm...
1 - Why do we need recommendation systems
Google News Recommendation Engine
https://tuoitre.vn/dbscl-chi-con-tren-muc-nuoc-bien-0-8m-nguy-co-di-tan-12-trieu-nguoi-20190905133103014.htm
Native Advertising with Recommended News
https://open.blogs.nytimes.com/2015/08/11/building-the-next-new-york-times-recommendation-engine/
Key ideas of News Recommendation System
Why Should We Use Recommendation Engines?
38% of click-through rates on Google News are
recommended links
The value of recommendation system
1. Retain user loyalty
2. Builds the volume of user traffic
3. Delivers best content expe...
So what is recommendation engine ?
In technical terms, a recommendation engine problem is to develop a
mathematical model ...
2 - How can we design
recommendation systems?
Popular techniques to build recommendation systems
1. Collaborative Filtering
2. Content-Based Filtering
3. Hybrid Recomme...
User-based Collaborative Filtering
● Based on a large amount of information on users’ behaviors, activities or
preferences...
User-based Collaborative Filtering
Content Based Filtering
( Item-based collaborative filtering)
Key ideas:
● Based on a description of the item and a profile...
Content Based Filtering
( Item-based collaborative filtering)
Hybrid Recommendation with USPA framework
These methods can be used to overcome some
of the common problems:
1. cold start...
Hybrid Recommendation Systems
3 - Case study with real social news data
https://www.bigdatavietnam.org/2019/09/how-to-build-personalized-news.html
Notes for designing news recommendation system
1. Novelty – In general, users tend to be more interested in the latest new...
Simple Recommendation Engine
with Apache Spark MLLib (https://spark.apache.org/mllib)
Follow this page to get more information
https://BigDataVietnam.org
https://facebook.com/BigDataVN
How to build a Personalized News Recommendation Platform
How to build a Personalized News Recommendation Platform
How to build a Personalized News Recommendation Platform
How to build a Personalized News Recommendation Platform
How to build a Personalized News Recommendation Platform
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How to build a Personalized News Recommendation Platform

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1) Why do we need recommendation systems ?
2) How can we design recommendation systems ?
3) Case study: News Recommendation with USPA.tech framework

Published in: Technology
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How to build a Personalized News Recommendation Platform

  1. 1. How to build a Personalized News Recommendation Platform By Nguyễn Tấn Triều (Thomas) Contact Email: tantrieuf31@gmail.com BigDataVietnam.org
  2. 2. Agenda 1. Why do we need recommendation systems ? 2. How can we design recommendation systems ? 3. Case study: News Recommendation with USPA framework
  3. 3. 1 - Why do we need recommendation systems
  4. 4. Google News Recommendation Engine
  5. 5. https://tuoitre.vn/dbscl-chi-con-tren-muc-nuoc-bien-0-8m-nguy-co-di-tan-12-trieu-nguoi-20190905133103014.htm
  6. 6. Native Advertising with Recommended News
  7. 7. https://open.blogs.nytimes.com/2015/08/11/building-the-next-new-york-times-recommendation-engine/
  8. 8. Key ideas of News Recommendation System
  9. 9. Why Should We Use Recommendation Engines? 38% of click-through rates on Google News are recommended links
  10. 10. The value of recommendation system 1. Retain user loyalty 2. Builds the volume of user traffic 3. Delivers best content experience to reader 4. Give your business a wider marketplace
  11. 11. So what is recommendation engine ? In technical terms, a recommendation engine problem is to develop a mathematical model or objective function which can predict how much a user will like an item. If U = {users}, I = {items} then F = Objective function and measures the usefulness of item I to user U, given by: F: U x I → R Where R = {recommended items}. For each user u, we want to choose the item i that maximizes the objective function:
  12. 12. 2 - How can we design recommendation systems?
  13. 13. Popular techniques to build recommendation systems 1. Collaborative Filtering 2. Content-Based Filtering 3. Hybrid Recommendation Systems with USPA framework
  14. 14. User-based Collaborative Filtering ● Based on a large amount of information on users’ behaviors, activities or preferences ● Predicting what users will like based on their similarity to other users. ● A key advantage: ○ accurately recommending complex items such as news without requiring an “understanding” of the item itself
  15. 15. User-based Collaborative Filtering
  16. 16. Content Based Filtering ( Item-based collaborative filtering) Key ideas: ● Based on a description of the item and a profile of the user’s preference. ● Keywords are used to describe the items ● User profile is built to indicate the type of item this user likes.
  17. 17. Content Based Filtering ( Item-based collaborative filtering)
  18. 18. Hybrid Recommendation with USPA framework These methods can be used to overcome some of the common problems: 1. cold start (no information on users’ behaviors) 2. sparsity problem (dense matrix)
  19. 19. Hybrid Recommendation Systems
  20. 20. 3 - Case study with real social news data
  21. 21. https://www.bigdatavietnam.org/2019/09/how-to-build-personalized-news.html
  22. 22. Notes for designing news recommendation system 1. Novelty – In general, users tend to be more interested in the latest news rather than in something that happened a long time ago. 2. User history – The latest news a user has read are very important to produce recommendations, because the user is intentionally showing interest on a topic or a set of topics. 3. Location – Users are more interested in news related to nearby events: the closer a user is to the place of the happening, more probably this can affect him. A news recommender system should then take into account the location where the action described in the piece of news took place. In a mobile environment scenario, user location has to be frequently updated and considered.
  23. 23. Simple Recommendation Engine with Apache Spark MLLib (https://spark.apache.org/mllib)
  24. 24. Follow this page to get more information https://BigDataVietnam.org https://facebook.com/BigDataVN

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