Modern Recommendation Systems in Real Applications
Phạm Hoàng Anh - AI Engineer - R&D LAB - SUN ASTERISK VIETNAM
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The Problems
Youtube
- 300 hours of video are uploaded to YouTube every minute!
- Almost 5 billion videos are watched on Youtube every single day.
- The total number of people who use YouTube 1,300,000,000 users
Amazon
- Sells more than 119,928,851 products
- In the U.S. alone, Amazon has over 150 million monthly unique visitors.
Viblo
- Over 26,000 verified users
- Over 21,000 published posts
- 1.5M Pageviews per month
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Recommendation System using Big Data
Store & Collect Data
Gathering Raw Data about Users' Activities
- Any events like history/return history, Cart events, Pageviews, Clicks and search log...
Analysis Data
Designing the analysis phase base on the application's requirements
- Time response, Data get and return stream,...
Filter and Recommend Items*
A core component of building a recommendation engine
- Using some algorithms to make good recommendations for users.
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Impact
Statistic from real world company
- 75% of what consumers watch on Netflix comes from the company’s recommender system
- Amazon credits recommender systems contribute 35% of their revenue
- Recommendations are responsible for 70% of the time people spend watching videos on YouTube.
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Core of Recommender Engine
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Content Based
Clustering
Collaborative
Session Based
Combination
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Content Based
- Most popular in RS
- Based on a description of the item to suggest similar items.
- Creating recommendations for items that have attributes but few user ratings
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Content Based
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Vectorizer/Embedding
- Bag of word
- TFIDF
- Item2vec
...
Clustering User
- Cluster Users
- Suggest items that popular with their group
- Not work right with new user and new item
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Clustering User
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Collaborative Filtering
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Collaborative Filtering
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Session Based
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Session Based
- Used by many famous E-commerce system (Alibaba, Youtube, Spotify...)
- Many challenge and research each year held
- Work with anonymous users
- Not work right with new item
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Viblo RS
Developing
max growth
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Thank you for your listening!
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