Part 1
1 - Requirements(Business Objective)
2 - Frame your ML Task
3 - Data Preparation
4 - Model Development
5 - Evaluation
6 - Deployment
7 - Monitoring
Business objective - increase the user engagement
- maximize the number of user clicks
- maximize the number of completed videos
- maximize the total watch time
----->>> maximize the number of relevant videos
user ---> video recommendation system --------> Video 1, video 2, video 3 .........
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videos recommended videos
Types of recommendation system
1 - Content-Based filtering
2 - Collaborative Filtering
3 - Hybrid Flitering
pros
- ability to recommend new videos
- ability to capture the unique interests of users
cons
- discover any new users interests
- you need domain knowledge
->> collaborative filtering do not use video features
pros
- no domain knowledge
- easy to discover users new area of interests
- efficient
cons
- cannot handle niche interests
Data Engineering
- video
- users
- user-video interactions
- video
metadata ->
videoID Length ManualTags ManualTitle Likes Views Language
1 30 Dog, Family my first day with my dog 151 5000 english
- users
ID Username Age Gender City Country Language Timezone
- user-video interactions
userID VideoID InteractionType InterationValue location timestamp
imperession 10 seconds
video features
- video ID
- duration
- language
- titles and tags
video feature preperation
- video ID - embedding -> [0.1,-0.5,0.2,0.7]
- duration - 130 ->
- language - english -> embedding
- titles - pre-trained model like BERT -> [0.1,0.2]
- tags - Continuous Bag Of Words(CBOW) - lightweight model
User feature preperation
- UserID -> embeddings
- age -> bucketize and one-hot encoding -> 20-24 (0), 25-30(1), 31-37(2)
- gender -> one-hot
- lagunage
- city
- country
contextual information
- time of the day
- device
- day of the week
user historical interaction
- search history
- liked
- watched video and imperessions