Recommender System and It's Design

What is a recommendation system? How recommendation system work?
The recommender system has a wide range of applications in the industry with movie, music, and product recommendations across top tech companies like Netflix, Spotify, Amazon, etc.
Consumers on the web are increasingly relying on recommendations to purchase the next product on Amazon watch the next CZcams or TikTok video or read the next post on LinkedIn. In short, recommendation systems make life easier by proactively surfacing content for consumers to consume, thus saving time and increasing customer satisfaction. This video will explain recommender systems and their designs.
By the end of the session, you will know:
- About Deep learning models for recommender systems
- Popular architectures for recommendation systems
- Deep-dive into design considerations for recommender systems
- How to design your own recommender system
- Recommender systems at Top Tech Companies
Table of Contents
0:00 Intro
1:50 Agenda
2:09 Introduction and Motivation for Recommender Systems
5:30 Why Recommender Systems?
7:13 Lay of the Land: Part 1 and Part 2
7:57 Question Break
9:13 Recap of Recommender Systems (Part 1)
20:04 Question Break
24:58 Recommender System Design and Architecture
40:05 Question Break
45:54 Popular Recommender Systems
53:40 Evaluating the Design for Recommender Systems
56:46 Summary
57:46 Q&A
This talk is a continuation of the previous talk (part 1), which introduced recommender systems (watch it here: • Recommender Systems: B... .
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#recommendersystems #deeplearning #ai

[@Datasciencedojo]

This talk is a continuation of the previous talk (part 1), which introduced recommender systems. watch it here: czcams.com/video/Ams4sEn50cw/video.html&ab_channel=DataScienceDojo

[@hasnainmamdani4534]

Excellent resource! Really enjoyed the depth covered in this hour-long video.
On the candidate generation model: how is a simple ML model trained on users, i.e., what are the inputs and output(s) for that simple ML model?

[@Datasciencedojo]

Hello Hasnain, a simple machine learning model for candidate generation is typically trained on a dataset of user interactions with items, such as clicks, views, or purchases. The inputs to the model would be features extracted from these interactions, such as the user ID, the item ID, the timestamp of the interaction, and any other relevant contextual information. The output of the model would be a probability score for each item, indicating the likelihood that the user would be interested in that item. This probability score can then be used to rank the items and generate a list of recommended candidates for the user. Hope this helps!

[@Datasciencedojo]

For further tutorials on advanced machine learning, check out this exclusive playlist: czcams.com/play/PL8eNk_zTBST_SS_czCz6Do1yrUowhKBHI.html

[@vinitv5081]

i wonder if netflix carefully uses the metrics because my feedback is that their recommendations always sucks

[@Datasciencedojo]

Absolutely, it can be really frustrating when the recommendations don't seem to align with our tastes. Netflix uses complex algorithms based on a variety of metrics, including viewing history, user ratings, and even time of day you watch. Sometimes, though, it feels like these don't capture our preferences accurately. Have you tried tweaking your profile or rating more shows and movies? It might help refine what's suggested to you. Also, it's interesting to think about how different users experience these systems differently.

[@ShivamGupta-qh8go]

this entire second session was just a recap of the first one... literally NOTHING NEW in this one